Journal articles: 'Multi-layer perceptron networks (MLPNs)'

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Przybył, Krzysztof, Krzysztof Koszela, Franciszek Adamski, Katarzyna Samborska, Katarzyna Walkowiak, and Mariusz Polarczyk. "Deep and Machine Learning Using SEM, FTIR, and Texture Analysis to Detect Polysaccharide in Raspberry Powders." Sensors 21, no. 17 (2021): 5823. http://dx.doi.org/10.3390/s21175823.

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In the paper, an attempt was made to use methods of artificial neural networks (ANN) and Fourier transform infrared spectroscopy (FTIR) to identify raspberry powders that are different from each other in terms of the amount and the type of polysaccharide. Spectra in the absorbance function (FTIR) were prepared as well as training sets, taking into account the structure of microparticles acquired from microscopic images with Scanning Electron Microscopy (SEM). In addition to the above, Multi-Layer Perceptron Networks (MLPNs) with a set of texture descriptors (machine learning) and Convolution Neural Network (CNN) with bitmap (deep learning) were devised, which is an innovative attitude to solving this issue. The aim of the paper was to create MLPN and CNN neural models, which are characterized by a high efficiency of classification. It translates into recognizing microparticles (obtaining their homogeneity) of raspberry powders on the basis of the texture of the image pixel.

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Rohman, Budiman Putra Asmaur, and Dayat Kurniawan. "Classification of Radar Environment Using Ensemble Neural Network with Variation of Hidden Neuron Number." Jurnal Elektronika dan Telekomunikasi 17, no. 1 (2017): 19. http://dx.doi.org/10.14203/jet.v17.19-24.

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Target detection is a mandatory task of radar system so that the radar system performance is mainly determined by its detection rate. Constant False Alarm Rate (CFAR) is a detection algorithm commonly used in radar systems. This method is divided into several approaches which have different performance in the different environments. Therefore, this paper proposes an ensemble neural network based classifier with a variation of hidden neuron number for classifying the radar environments. The result of this research will support the improvement of the performance of the target detection on the radar systems by developing such an adaptive CFAR. Multi-layer perceptron network (MLPN) with a single hidden layer is employed as the structure of base classifiers. The first step of this research is the evaluation of the hidden neuron number giving the highest accuracy of classification and the simplicity of computation. According to the result of this step, the three best structures are selected to build an ensemble classifier. On the ensemble structure, all of those three MLPN outputs then be collected and voted for getting the majority result in order to decide the final classification. The three possible radar environments investigated are homogeneous, multiple-targets and clutter boundary. According to the simulation results, the ensemble MLPN provides a higher detection rate than the conventional single MLPNs. Moreover, in the multiple-target and clutter boundary environments, the proposed method is able to show its highest performance.

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Bologna, Guido. "A Simple Convolutional Neural Network with Rule Extraction." Applied Sciences 9, no. 12 (2019): 2411. http://dx.doi.org/10.3390/app9122411.

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Classification responses provided by Multi Layer Perceptrons (MLPs) can be explained by means of propositional rules. So far, many rule extraction techniques have been proposed for shallow MLPs, but not for Convolutional Neural Networks (CNNs). To fill this gap, this work presents a new rule extraction method applied to a typical CNN architecture used in Sentiment Analysis (SA). We focus on the textual data on which the CNN is trained with “tweets” of movie reviews. Its architecture includes an input layer representing words by “word embeddings”, a convolutional layer, a max-pooling layer, followed by a fully connected layer. Rule extraction is performed on the fully connected layer, with the help of the Discretized Interpretable Multi Layer Perceptron (DIMLP). This transparent MLP architecture allows us to generate symbolic rules, by precisely locating axis-parallel hyperplanes. Experiments based on cross-validation emphasize that our approach is more accurate than that based on SVMs and decision trees that substitute DIMLPs. Overall, rules reach high fidelity and the discriminative n-grams represented in the antecedents explain the classifications adequately. With several test examples we illustrate the n-grams represented in the activated rules. They present the particularity to contribute to the final classification with a certain intensity.

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CAIRNS, GRAHAM, and LIONEL TARASSENKO. "PERTURBATION TECHNIQUES FOR ON-CHIP LEARNING WITH ANALOGUE VLSI MLPs." Journal of Circuits, Systems and Computers 06, no. 02 (1996): 93–113. http://dx.doi.org/10.1142/s0218126696000108.

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Microelectronic neural network technology has become sufficiently mature over the past few years that reliable performance can now be obtained from VLSI circuits under carefully controlled conditions (see Refs. 8 or 13 for example). The use of analogue VLSI allows low power, area efficient hardware realisations which can perform the computationally intensive feed-forward operation of neural networks at high speed, making real-time applications possible. In this paper we focus on important issues for the successful operation and implementation of on-chip learning with such analogue VLSI neural hardware, in particular the issue of weight precision. We first review several perturbation techniques which have been proposed to train multi-layer perceptron (MLP) networks. We then present a novel error criterion which performs well on benchmark problems and which allows simple integration of error measurement hardware for complete on-chip learning systems.

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Suprapto, Suprapto, and Edy Riyanto. "Grape Drying Process Using Machine Vision Based on Multilayer Perceptron Networks." Indonesian Journal of Science and Technology 5, no. 3 (2020): 382–94. http://dx.doi.org/10.17509/ijost.v5i3.24991.

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This paper proposed a grape drying machine using computer vision and Multi-layer Perceptron (MLP) method. Computer vision is for taking grapes’ image on conveyor, whereas MLP is for controlling grape drying machine and classifying its output. To evaluate the proposed, a kind of grapes are put on conveyor of the machine and their images are taken every two min. Some parameters of MLP to control the drying machine includes dried grape, temperature, grape area, motor position, and motion speed. Those parameters are to adjust an appropriate MLP’s output, including motion control and heater control. Two different temperatures are employed on the machine, including 60 and 75°C. The results showed that the grape could be dried with similar area 3800 pixel at the 770th min using temperature 60°C and at the 410th min using temperature 75°C. Comparing between them, the similar ratio could also be achieved at 0.64 with different time 360 min. Indeed, the temperature setting at 75°C resulted faster drying performance.

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Geng, Chao, Qingji Sun, and Shigetoshi Nakatake. "Implementation of Analog Perceptron as an Essential Element of Configurable Neural Networks." Sensors 20, no. 15 (2020): 4222. http://dx.doi.org/10.3390/s20154222.

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Perceptron is an essential element in neural network (NN)-based machine learning, however, the effectiveness of various implementations by circuits is rarely demonstrated from chip testing. This paper presents the measured silicon results for the analog perceptron circuits fabricated in a 0.6 μm/±2.5 V complementary metal oxide semiconductor (CMOS) process, which are comprised of digital-to-analog converter (DAC)-based multipliers and phase shifters. The results from the measurement convinces us that our implementation attains the correct function and good performance. Furthermore, we propose the multi-layer perceptron (MLP) by utilizing analog perceptron where the structure and neurons as well as weights can be flexibly configured. The example given is to design a 2-3-4 MLP circuit with rectified linear unit (ReLU) activation, which consists of 2 input neurons, 3 hidden neurons, and 4 output neurons. Its experimental case shows that the simulated performance achieves a power dissipation of 200 mW, a range of working frequency from 0 to 1 MHz, and an error ratio within 12.7%. Finally, to demonstrate the feasibility and effectiveness of our analog perceptron for configuring a MLP, seven more analog-based MLPs designed with the same approach are used to analyze the simulation results with respect to various specifications, in which two cases are used to compare to their digital counterparts with the same structures.

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Bensaoucha, Saddam, Youcef Brik, Sandrine Moreau, Sid Ahmed Bessedik, and Aissa Ameur. "Induction machine stator short-circuit fault detection using support vector machine." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 40, no. 3 (2021): 373–89. http://dx.doi.org/10.1108/compel-06-2020-0208.

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Purpose This paper provides an effective study to detect and locate the inter-turn short-circuit faults (ITSC) in a three-phase induction motor (IM) using the support vector machine (SVM). The characteristics extracted from the analysis of the phase shifts between the stator currents and their corresponding voltages are used as inputs to train the SVM. The latter automatically decides on the IM state, either a healthy motor or a short-circuit fault on one of its three phases. Design/methodology/approach To evaluate the performance of the SVM, three supervised algorithms of machine learning, namely, multi-layer perceptron neural networks (MLPNNs), radial basis function neural networks (RBFNNs) and extreme learning machine (ELM) are used along with the SVM in this study. Thus, all classifiers (SVM, MLPNN, RBFNN and ELM) are tested and the results are compared with the same data set. Findings The obtained results showed that the SVM outperforms MLPNN, RBFNNs and ELM to diagnose the health status of the IM. Especially, this technique (SVM) provides an excellent performance because it is able to detect a fault of two short-circuited turns (early detection) when the IM is operating under a low load. Originality/value The original of this work is to use the SVM algorithm based on the phase shift between the stator currents and their voltages as inputs to detect and locate the ITSC fault.

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Loukeris, Nikolaos, and Iordanis Eleftheriadis. "Further Higher Moments in Portfolio Selection andA PrioriDetection of Bankruptcy, Under Multi-layer Perceptron Neural Networks, Hybrid Neuro-genetic MLPs, and the Voted Perceptron." International Journal of Finance & Economics 20, no. 4 (2015): 341–61. http://dx.doi.org/10.1002/ijfe.1521.

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Przybył, Krzysztof, Jolanta Wawrzyniak, Krzysztof Koszela, Franciszek Adamski, and Marzena Gawrysiak-Witulska. "Application of Deep and Machine Learning Using Image Analysis to Detect Fungal Contamination of Rapeseed." Sensors 20, no. 24 (2020): 7305. http://dx.doi.org/10.3390/s20247305.

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This paper endeavors to evaluate rapeseed samples obtained in the process of storage experiments with different humidity (12% and 16% seed moisture content) and temperature conditions (25 and 30 °C). The samples were characterized by different levels of contamination with filamentous fungi. In order to acquire graphic data, the analysis of the morphological structure of rapeseeds was carried out with the use of microscopy. The acquired database was prepared in order to build up training, validation, and test sets. The process of generating a neural model was based on Convolutional Neural Networks (CNN), Multi-Layer Perceptron Networks (MLPN), and Radial Basis Function Networks (RBFN). The classifiers that were compared were devised on the basis of the environments Tensorflow (deep learning) and Statistica (machine learning). As a result, it was possible to achieve the lowest classification error of 14% for the test set, 18% classification error for MLPN, and 21% classification error for RBFN, in the process of recognizing mold in rapeseed with the use of CNN.

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He, Hao, Jiaxiang Zhao, and Guiling Sun. "Prediction of MoRFs in Protein Sequences with MLPs Based on Sequence Properties and Evolution Information." Entropy 21, no. 7 (2019): 635. http://dx.doi.org/10.3390/e21070635.

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Molecular recognition features (MoRFs) are one important type of intrinsically disordered proteins functional regions that can undergo a disorder-to-order transition through binding to their interaction partners. Prediction of MoRFs is crucial, as the functions of MoRFs are associated with many diseases and can therefore become the potential drug targets. In this paper, a method of predicting MoRFs is developed based on the sequence properties and evolutionary information. To this end, we design two distinct multi-layer perceptron (MLP) neural networks and present a procedure to train them. We develop a preprocessing process which exploits different sizes of sliding windows to capture various properties related to MoRFs. We then use the Bayes rule together with the outputs of two trained MLP neural networks to predict MoRFs. In comparison to several state-of-the-art methods, the simulation results show that our method is competitive.

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Gierz, Łukasz, Krzysztof Przybył, Krzysztof Koszela, Adamina Duda, and Witold Ostrowicz. "The Use of Image Analysis to Detect Seed Contamination—A Case Study of Triticale." Sensors 21, no. 1 (2020): 151. http://dx.doi.org/10.3390/s21010151.

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Samples of triticale seeds of various qualities were assessed in the study. The seeds were obtained during experiments, reflecting the actual sowing conditions. The experiments were conducted on an original test facility designed by the authors of this study. The speed of the air (15, 20, 25 m/s) transporting seeds in the pneumatic conduit was adjusted to sowing. The resulting graphic database enabled the distinction of six classes of seeds according to their quality and sowing speed. The database was prepared to build training, validation and test sets. The neural model generation process was based on multi-layer perceptron networks (MLPN) and statistical (machine training). When the MLPN was used to identify contaminants in seeds sown at a speed of 15 m/s, the lowest RMS error of 0.052 was noted, whereas the classification correctness coefficient amounted to 0.99.

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Arshad, R. Rezaei, Gh Sayyad, M. Mosaddeghi, and B. Gharabaghi. "Predicting Saturated Hydraulic Conductivity by Artificial Intelligence and Regression Models." ISRN Soil Science 2013 (June 11, 2013): 1–8. http://dx.doi.org/10.1155/2013/308159.

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Saturated hydraulic conductivity (Ks), among other soil hydraulic properties, is important and necessary in water and mass transport models and irrigation and drainage studies. Although this property can be measured directly, its measurement is difficult and very variable in space and time. Thus pedotransfer functions (PTFs) provide an alternative way to predict the Ks from easily available soil data. This study was done to predict the Ks in Khuzestan province, southwest Iran. Three Intelligence models including (radial basis function neural networks (RBFNN), multi layer perceptron neural networks (MLPNN)), adaptive neuro-fuzzy inference system (ANFIS) and multiple-linear regression (MLR) to predict the Ks were used. Input variable included sand, silt, and clay percents and bulk density. The total of 175 soil samples was divided into two groups as 130 for the training and 45 for the testing of PTFs. The results indicated that ANFIS and RBFNN are effective methods for Ks prediction and have better accuracy compared with the MLPNN and MLR models. The correlation between predicted and measured Ks values using ANFIS was better than artificial neural network (ANN). Mean square error values for ANFIS, ANN, and MLR were 0.005, 0.02, and 0.17, respectively, which shows that ANFIS model is a powerful tool and has better performance than ANN and MLR in prediction of Ks.

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Chen, Can, Luca Zanotti Fragonara, and Antonios Tsourdos. "Go Wider: An Efficient Neural Network for Point Cloud Analysis via Group Convolutions." Applied Sciences 10, no. 7 (2020): 2391. http://dx.doi.org/10.3390/app10072391.

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In order to achieve a better performance for point cloud analysis, many researchers apply deep neural networks using stacked Multi-Layer-Perceptron (MLP) convolutions over an irregular point cloud. However, applying these dense MLP convolutions over a large amount of points (e.g., autonomous driving application) leads to limitations due to the computation and memory capabilities. To achieve higher performances but decrease the computational complexity, we propose a deep-wide neural network, named ShufflePointNet, which can exploit fine-grained local features, but also reduce redundancies using group convolution and channel shuffle operation. Unlike conventional operations that directly apply MLPs on the high-dimensional features of a point cloud, our model goes “wider” by splitting features into groups with smaller depth in advance, having the respective MLP computations applied only to a single group, which can significantly reduce complexity and computation. At the same time, we allow communication between groups by shuffling the feature channel to capture fine-grained features. We further discuss the multi-branch method for wider neural networks being also beneficial to feature extraction for point clouds. We present extensive experiments for shape classification tasks on a ModelNet40 dataset and semantic segmentation task on large scale datasets ShapeNet part, S3DIS and KITTI. Finally, we carry out an ablation study and compare our model to other state-of-the-art algorithms to show its efficiency in terms of complexity and accuracy.

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Kim, Taehwan, and Tülay Adalı. "Approximation by Fully Complex Multilayer Perceptrons." Neural Computation 15, no. 7 (2003): 1641–66. http://dx.doi.org/10.1162/089976603321891846.

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We investigate the approximation ability of a multi layer perceptron (MLP) network when it is extended to the complex domain. The main challenge for processing complex data with neural networks has been the lack of bounded and analytic complex nonlinear activation functions in the complex domain, as stated by Liouville's theorem. To avoid the conflict between the boundedness and the analyticity of a nonlinear complex function in the complex domain, a number of ad hoc MLPs that include using two real-valued MLPs, one processing the real part and the other processing the imaginary part, have been traditionally employed. However, since nonanalytic functions do not meet the Cauchy-Riemann conditions, they render themselves into degenerative backpropagation algorithms that compromise the efficiency of nonlinear approximation and learning in the complex vector field. A number of elementary transcendental functions (ETFs) derivable from the entire exponential functionez that are analytic are defined as fully complex activation functions and are shown to provide a parsimonious structure for processing data in the complex domain and address most of the shortcomings of the traditional approach. The introduction of ETFs, however, raises a new question in the approximation capability of this fully complex MLP. In this letter, three proofs of the approximation capability of the fully complex MLP are provided based on the characteristics of singularity among ETFs. First, the fully complex MLPs with continuous ETFs over a compact set in the complex vector field are shown to be the universal approximator of any continuous complex mappings. The complex universal approximation theorem extends to bounded measurable ETFs possessing a removable singularity. Finally, it is shown that the output of complex MLPs using ETFs with isolated and essential singularities uniformly converges to any nonlinear mapping in the deleted annulus of singularity nearest to the origin.

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Kaur, Jatinder, Dr Mandeep Singh, Pardeep Singh Bains, and Gagandeep Singh. "Analysis of Multi layer Perceptron Network." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 7, no. 2 (2013): 600–606. http://dx.doi.org/10.24297/ijct.v7i2.3462.

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In this paper, we introduce the multilayer Perceptron (feedforward) neural network (MLPs) and used it for a function approximation. For the training of MLP, we have used back propagation algorithm principle. The main purpose of this paper lies in changing the number of hidden layers of MLP for achieving minimum value of mean square error.

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LIANG, XUN, and SHAOWEI XIA. "METHODS OF TRAINING AND CONSTRUCTING MULTILAYER PERCEPTRONS WITH ARBITRARY PATTERN SETS." International Journal of Neural Systems 06, no. 03 (1995): 233–47. http://dx.doi.org/10.1142/s0129065795000172.

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This paper presents two compensation methods for multilayer perceptrons (MLPs) which are very difficult to train by traditional Back Propagation (BP) methods. For MLPs trapped in local minima, compensating methods can correct the wrong outputs one by one using constructing techniques until all outputs are right, so that the MLPs can skip from the local minima to the global minima. A hidden neuron is added as compensation for a binary input three-layer perceptron trapped in a local minimum; and one or two hidden neurons are added as compensation for a real input three-layer perceptron. For a perceptron of more than three layers, the second hidden layer from behind will be temporarily treated as the input layer during compensation, hence the above methods can also be used. Examples are given.

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Xi, Yan Hui, and Hui Peng. "Training Multi-Layer Perceptrons with the Unscented Kalman Particle Filter." Advanced Materials Research 542-543 (June 2012): 745–48. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.745.

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Many Bayesian learning approaches to multi-layer perceptrons (MLPs) parameters optimization have been proposed such as the extended Kalman filter (EKF). In this paper, a sequential approach is applied to train the MLPs. Based on the particle filter, the approach named unscented Kalman particle filter (UPF) uses the unscented Kalman filter as proposal distribution to generate the importance sampling density. The UPF are devised to deal with the high dimensional parameter space that is inherent to neural network models. Simulation results show that the new algorithm performs better than traditional optimization methods such as the extended Kalman filter.

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BUCHHOLZ, SVEN, and NICOLAS LE BIHAN. "POLARIZED SIGNAL CLASSIFICATION BY COMPLEX AND QUATERNIONIC MULTI-LAYER PERCEPTRONS." International Journal of Neural Systems 18, no. 02 (2008): 75–85. http://dx.doi.org/10.1142/s0129065708001403.

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For polarized signals, which arise in many application fields, a statistical framework in terms of quaternionic random processes is proposed. Based on it, the ability of real-, complex- and quaternionic-valued multi-layer perceptrons (MLPs) of performing classification tasks for such signals is evaluated. For the multi-dimensional neural networks the relevance of class label representations is discussed. For signal to noise separation it is shown that the quaternionic MLP yields an optimal solution. Results on the classification of two different polarized signals are also reported.

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LEHTOKANGAS, MIKKO. "FAST LEARNING USING MULTILAYER PERCEPTRON NETWORKS WITH ADAPTIVE CENTROID LAYER." International Journal of Pattern Recognition and Artificial Intelligence 14, no. 02 (2000): 211–23. http://dx.doi.org/10.1142/s0218001400000143.

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A hybrid neural network architecture is investigated for classification purposes. The proposed hybrid is based on the multilayer perceptron (MLP) network. In addition to the usual hidden layers the first hidden layer is selected to be an adaptive centroid layer. Each unit in this new layer incorporates a centroid vector that is located somewhere in the space spanned by the input variables. The output of these units is the Euclidean distance between the centroid vector and the inputs. The centroid layer has some resemblance to the hidden layer of the radial basis function (RBF) networks. Therefore the proposed design can be regarded as a sort of hybrid of the MLP and RBF networks. The presented benchmark experiments demonstrate that the proposed hybrid can provide significant advantages over standard MLPs in terms of fast and efficient learning, and compact network structure.

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Mühlenbein, H. "Limitations of multi-layer perceptron networks - steps towards genetic neural networks." Parallel Computing 14, no. 3 (1990): 249–60. http://dx.doi.org/10.1016/0167-8191(90)90079-o.

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Achili, B., B. Daachi, Y. Amirat, A. Ali-Cherif, and M. E. Daâchi. "A stable adaptive force/position controller for a C5 parallel robot: a neural network approach." Robotica 30, no. 7 (2012): 1177–87. http://dx.doi.org/10.1017/s0263574711001354.

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SUMMARYThis paper presents an adaptive force/position controller for a parallel robot executing constrained motions. This controller, based on an MLPNN (or Multi-Layer Perceptron Neural Network), does not require the inverse dynamic model of the robot to derive the control law. A neural identification of the dynamic model of the robot is proposed to determine the principal components of the MLPNN input vector. The latter is used to compensate the dynamic effects arising from the robot–environment interaction and its parameters are adjusted according to an adaptation law based on the Lyapunov-analysis methodology. The proposed controller is evaluated experimentally on the C5 parallel robot. This method is capable of tracking accurately the force/position trajectories and its stability robustness is proved.

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Payganeh, Gholam Hassan, Mehrdad Nouri Khajavi, Reza Ebrahimpour, and Ebrahim Babaei. "Machine Fault Diagnosis Using MLPs and RBF Neural Networks." Applied Mechanics and Materials 110-116 (October 2011): 5021–28. http://dx.doi.org/10.4028/www.scientific.net/amm.110-116.5021.

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-Fault detection and elimination in industrial machineries can help prevent loss of life and financial assets. In this study four common faults in rotating machineries namely: 1) Mass Unbalance 2) Angular Misalignment 3) Bearing Faults and 4) Mechanical Looseness have been considered. Each of these defects has been created separately on a test rig comprising of an electrical motor coupled to a rotor assembly. A Vibrotest 60 vibration spectrum analyzer has been used to collect velocity spectrum of the vibration on the bearings. Eleven characteristic features have been chosen to distinguish different faults. Based on the acquired data an Artificial Neural Network Multi Layer Perceptrons (MLPs) and Radial Basis Functions (RBF) Neural Network has been designed to recognize each one of the aforementioned defects. After training the Neural Network, it was checked by new data gathered by new experiments and the results showed that the designed network can predict the faults with more than 75% reliability, and it can be a good assistance to an ordinary machine operator to guess the problem and hence make a good decision.

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MÜGE, DURSUN, ŞENOL YAVUZ, BULGUN ENDER YAZGAN, and AKKAN TANER. "Neural network based thermal protective performance prediction of three-layered fabrics for firefighter clothing." Industria Textila 70, no. 01 (2019): 57–64. http://dx.doi.org/10.35530/it.070.01.1527.

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The firefighter protective clothing is comprised of three main layers; an outer shell, a moisture barrier and a thermal liner. This three-layered fabric structure provides protection against the fire and extremely hot environments. Various parameters such as fabric construction, weight, warp/weft count, warp/weft density, thickness, water vapour resistance of the fabric layers have effect on the protective performance as heat transfer through the firefighter clothing. In this study, it is aimed to examine the predictability of the heat transfer index of three-layered fabrics, as function of the fabric parameters using artificial neural networks. Therefore, 64 different three layered-fabric assembly combinations of the firefighter clothing were obtained and the convective heat transfer (HTI) and radiant heat transfer (RHTI) through the fabric combinations were measured in a laboratory. Six multilayer perceptron neural networks (MLPNN) each with a single hidden layer and the same 12 input data were constructed to predict the convective heat transfer performance and the radiant heat transfer performance of three-layered fabrics separately. The networks 1 to 4 were trained to predict HTI12, HTI24, RHTI12, and RHTI24, respectively, while networks 5 and 6 had two outputs, HTI12 and HTI24, and RHTI12 and RHTI24, respectively. Each system indicates a good correlation between the predicted values and the experimental values. The results demonstrate that the proposed MLPNNs are able to predict the convective heat transfer and the radiant heat transfer effectively. However, the neural network with two outputs has slightly better prediction performance

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Than, Nguyen Hien. "WATER QUALITY CLASSIFICATION BY ARTIFICIAL NEURAL NETWORK - A CASE STUDY OF DONG NAI RIVER, VIETNAM." Vietnam Journal of Science and Technology 55, no. 4C (2018): 297. http://dx.doi.org/10.15625/2525-2518/55/4c/12167.

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The Dong Nai River is the main source of supplied water for Ho Chi Minh City, Dong Nai, Binh Duong province and other areas. However, the water quality state of the Dong Nai River has been heavily pressured by discharged sources from urban areas, industrial zones, agricultural, domestic activities, etc. In this paper, the authors employed the artificial neural network model (ANNs) to classify water quality of Dong Nai River that apply a new tool to assess water quality in Vietnam. The monitoring data were used for eight years from 2007 to 2014 with 23 monitoring stations. Two neural network models including a multi-layer perceptron (MLPNN) and a generalized regression network (GRNN) were employed to classify water quality of the Dong Nai River. The results of the study showed that GRNN and MLPNN classified excellently water quality. Optimal structure of the MLPNN was H8I4O1 with model error about 0.1268 while the GRNN was error about 0.00001615. Comparing the result of water quality classification between the ANNs and the fuzzy comprehensive evaluation indicated that they were in close agreement with the respective values (the accurate rate of GRNN 100% and 98,5 % of MLPNN).

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LEHTOKANGAS, MIKKO. "FEEDFORWARD NEURAL NETWORK WITH ADAPTIVE REFERENCE PATTERN LAYER." International Journal of Neural Systems 09, no. 01 (1999): 1–9. http://dx.doi.org/10.1142/s0129065799000022.

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A hybrid neural network architecture is investigated for modeling purposes. The proposed hybrid is based on the multilayer perceptron (MLP) network. In addition to the usual hidden layers, the first hidden layer is selected to be an adaptive reference pattern layer. Each unit in this new layer incorporates a reference pattern that is located somewhere in the space spanned by the input variables. The outputs of these units are the component wise-squared differences between the elements of a reference pattern and the inputs. The reference pattern layer has some resemblance to the hidden layer of the radial basis function (RBF) networks. Therefore the proposed design can be regarded as a sort of hybrid of MLP and RBF networks. The presented benchmark experiments show that the proposed hybrid can provide significant advantages over standard MLPs and RBFs in terms of fast and efficient learning, and compact network structure.

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Çaylak, Çağrı, and İlknur Kaftan. "Determination of near-surface structures from multi-channel surface wave data using multi-layer perceptron neural network (MLPNN) algorithm." Acta Geophysica 62, no. 6 (2014): 1310–27. http://dx.doi.org/10.2478/s11600-014-0207-8.

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Khotanzad, A., and C. Chung. "Application of multi-layer perceptron neural networks to vision problems." Neural Computing & Applications 7, no. 3 (1998): 249–59. http://dx.doi.org/10.1007/bf01414886.

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Jou, I. Chang, Shih-Shien You, and Long-Wen Chang. "Analysis of hidden nodes for multi-layer perceptron neural networks." Pattern Recognition 27, no. 6 (1994): 859–64. http://dx.doi.org/10.1016/0031-3203(94)90170-8.

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Bachtiar, Luqman R., Charles P. Unsworth, Richard D. Newcomb, and Edmund J. Crampin. "Multilayer Perceptron Classification of Unknown Volatile Chemicals from the Firing Rates of Insect Olfactory Sensory Neurons and Its Application to Biosensor Design." Neural Computation 25, no. 1 (2013): 259–87. http://dx.doi.org/10.1162/neco_a_00386.

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In this letter, we use the firing rates from an array of olfactory sensory neurons (OSNs) of the fruit fly, Drosophila melanogaster, to train an artificial neural network (ANN) to distinguish different chemical classes of volatile odorants. Bootstrapping is implemented for the optimized networks, providing an accurate estimate of a network's predicted values. Initially a simple linear predictor was used to assess the complexity of the data and was found to provide low prediction performance. A nonlinear ANN in the form of a single multilayer perceptron (MLP) was also used, providing a significant increase in prediction performance. The effect of the number of hidden layers and hidden neurons of the MLP was investigated and found to be effective in enhancing network performance with both a single and a double hidden layer investigated separately. A hybrid array of MLPs was investigated and compared against the single MLP architecture. The hybrid MLPs were found to classify all vectors of the validation set, presenting the highest degree of prediction accuracy. Adjustment of the number of hidden neurons was investigated, providing further performance gain. In addition, noise injection was investigated, proving successful for certain network designs. It was found that the best-performing MLP was that of the double-hidden-layer hybrid MLP network without the use of noise injection. Furthermore, the level of performance was examined when different numbers of OSNs used were varied from the maximum of 24 to only 5 OSNs. Finally, the ideal OSNs were identified that optimized network performance. The results obtained from this study provide strong evidence of the usefulness of ANNs in the field of olfaction for the future realization of a signal processing back end for an artificial olfactory biosensor.

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Nortje, Wimpie D., Johann E. W. Holm, Gerhard P. Hancke, Imre J. Rudas, and Laszlo Horvath. "Results of Bias-variance Tests on Multi-layer Perceptron Neural Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 5 (2001): 300–305. http://dx.doi.org/10.20965/jaciii.2001.p0300.

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Training neural networks involves selection of a set of network parameters, or weights, on account of fitting a non-linear model to data. Due to the bias in the training data and small computational errors, the neural networks’ opinions are biased. Some improvement is possible when multiple networks are used to do the classification. This approach is similar to taking the average of a number of biased opinions in order to remove some of the bias that resulted from training. Bayesian networks are effective in removing some of the bias associated with training, but Bayesian techniques are tedious in terms of computational time. It is for this reason that alternatives to Bayesian networks are investigated.

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Śmieja, F. J., and H. Mühlenbein. "The geometry of multi-layer perceptron solutions." Parallel Computing 14, no. 3 (1990): 261–75. http://dx.doi.org/10.1016/0167-8191(90)90080-s.

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Savalia, Shalin, and Vahid Emamian. "Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks." Bioengineering 5, no. 2 (2018): 35. http://dx.doi.org/10.3390/bioengineering5020035.

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Sun, Wenzheng, Qichun Wei, Lei Ren, Jun Dang, and Fang-Fang Yin. "Adaptive respiratory signal prediction using dual multi-layer perceptron neural networks." Physics in Medicine & Biology 65, no. 18 (2020): 185005. http://dx.doi.org/10.1088/1361-6560/abb170.

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Bologna, Guido, and Yoichi Hayashi. "Characterization of Symbolic Rules Embedded in Deep DIMLP Networks: A Challenge to Transparency of Deep Learning." Journal of Artificial Intelligence and Soft Computing Research 7, no. 4 (2017): 265–86. http://dx.doi.org/10.1515/jaiscr-2017-0019.

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AbstractRule extraction from neural networks is a fervent research topic. In the last 20 years many authors presented a number of techniques showing how to extract symbolic rules from Multi Layer Perceptrons (MLPs). Nevertheless, very few were related to ensembles of neural networks and even less for networks trained by deep learning. On several datasets we performed rule extraction from ensembles of Discretized Interpretable Multi Layer Perceptrons (DIMLP), and DIMLPs trained by deep learning. The results obtained on the Thyroid dataset and the Wisconsin Breast Cancer dataset show that the predictive accuracy of the extracted rules compare very favorably with respect to state of the art results. Finally, in the last classification problem on digit recognition, generated rules from the MNIST dataset can be viewed as discriminatory features in particular digit areas. Qualitatively, with respect to rule complexity in terms of number of generated rules and number of antecedents per rule, deep DIMLPs and DIMLPs trained by arcing give similar results on a binary classification problem involving digits 5 and 8. On the whole MNIST problem we showed that it is possible to determine the feature detectors created by neural networks and also that the complexity of the extracted rulesets can be well balanced between accuracy and interpretability.

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Lukito, Yuan. "Multi Layer Perceptron Model for Indoor Positioning System Based on Wi-Fi." Jurnal Teknologi dan Sistem Komputer 5, no. 3 (2017): 123–28. http://dx.doi.org/10.14710/jtsiskom.5.3.2017.123-128.

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Indoor positioning system issue is an open problem that still needs some improvements. This research explores the utilization of multilayer perceptron in determining someone’s position inside a building or a room, which generally known as Indoor Positioning System. The research was conducted in some steps: dataset normalization, multilayer perceptron implementation, training process of multilayer perceptron, evaluation, and analysis. The training process has been conducted many times to find the best parameters that produce the best accuracy rate. The experiment produces 79,16% as the highest accuracy rate. Compared to previous research, this result is comparably lower and needs some parameters tweaking or changing the neural networks architectures.

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K.S., Sree Ranjini. "A study on performance of MHDA in training MLPs." Engineering Computations 36, no. 6 (2019): 1820–34. http://dx.doi.org/10.1108/ec-05-2018-0216.

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Purpose In recent years, the application of metaheuristics in training neural network models has gained significance due to the drawbacks of deterministic algorithms. This paper aims to propose the use of a recently developed “memory based hybrid dragonfly algorithm” (MHDA) for training multi-layer perceptron (MLP) model by finding the optimal set of weight and biases. Design/methodology/approach The efficiency of MHDA in training MLPs is evaluated by applying it to classification and approximation benchmark data sets. Performance comparison between MHDA and other training algorithms is carried out and the significance of results is proved by statistical methods. The computational complexity of MHDA trained MLP is estimated. Findings Simulation result shows that MHDA can effectively find the near optimum set of weight and biases at a higher convergence rate when compared to other training algorithms. Originality/value This paper presents MHDA as an alternative optimization algorithm for training MLP. MHDA can effectively optimize set of weight and biases and can be a potential trainer for MLPs.

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Jadidi, Aydin, Raimundo Menezes, Nilmar de Souza, and Antonio Cezar de Castro Lima. "Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model." Energies 12, no. 10 (2019): 1891. http://dx.doi.org/10.3390/en12101891.

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Load forecasting is of crucial importance for smart grids and the electricity market in terms of the meeting the demand for and distribution of electrical energy. This research proposes a hybrid algorithm for improving the forecasting accuracy where a non-dominated sorting genetic algorithm II (NSGA II) is employed for selecting the input vector, where its fitness function is a multi-layer perceptron neural network (MLPNN). Thus, the output of the NSGA II is the output of the best-trained MLPNN which has the best combination of inputs. The result of NSGA II is fed to the Adaptive Neuro-Fuzzy Inference System (ANFIS) as its input and the results demonstrate an improved forecasting accuracy of the MLPNN-ANFIS compared to the MLPNN and ANFIS models. In addition, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), differential evolution (DE), and imperialistic competitive algorithm (ICA) are used for optimized design of the ANFIS. Electricity demand data for Bonneville, Oregon are used to test the model and among the different tested models, NSGA II-ANFIS-GA provides better accuracy. Obtained values of error indicators for one-hour-ahead demand forecasting are 107.2644, 1.5063, 65.4250, 1.0570, and 0.9940 for RMSE, RMSE%, MAE, MAPE, and R, respectively.

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Jadidi, Aydin, Raimundo Menezes, Nilmar de Souza, and Antonio de Castro Lima. "A Hybrid GA–MLPNN Model for One-Hour-Ahead Forecasting of the Global Horizontal Irradiance in Elizabeth City, North Carolina." Energies 11, no. 10 (2018): 2641. http://dx.doi.org/10.3390/en11102641.

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The use of photovoltaics is still considered to be challenging because of certain reliability issues and high dependence on the global horizontal irradiance (GHI). GHI forecasting has a wide application from grid safety to supply–demand balance and economic load dispatching. Given a data set, a multi-layer perceptron neural network (MLPNN) is a strong tool for solving the forecasting problems. Furthermore, noise detection and feature selection in a data set with numerous variables including meteorological parameters and previous values of GHI are of crucial importance to obtain the desired results. This paper employs density-based spatial clustering of applications with noise (DBSCAN) and non-dominated sorting genetic algorithm II (NSGA II) algorithms for noise detection and feature selection, respectively. Tuning the neural network is another important issue that includes choosing the hidden layer size and activation functions between the layers of the network. Previous studies have utilized a combination of different parameters based on trial and error, which seems to be inefficient in terms of accurate selection of the desired features and also tuning of the neural network. In this research, two different methods—namely, particle swarm optimization (PSO) algorithm and genetic algorithm (GA)—are utilized in order to tune the MLPNN, and the results of one-hour-ahead forecasting of the GHI are subsequently compared. The methodology is validated using the hourly data for Elizabeth City located in North Carolina, USA, and the results demonstrated a better performance of GA in comparison with PSO. The GA-tuned MLPNN reported a normalized root mean square error (nRMSE) of 0.0458 and a normalized mean absolute error (nMAE) of 0.0238.

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HAYASHI, YOICHI. "NEURAL NETWORK RULE EXTRACTION BY A NEW ENSEMBLE CONCEPT AND ITS THEORETICAL AND HISTORICAL BACKGROUND: A REVIEW." International Journal of Computational Intelligence and Applications 12, no. 04 (2013): 1340006. http://dx.doi.org/10.1142/s1469026813400063.

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This paper presents theoretical and historical backgrounds related to neural network rule extraction. It also investigates approaches for neural network rule extraction by ensemble concepts. Bologna pointed out that although many authors had generated comprehensive models from individual networks, much less work had been done to explain ensembles of neural networks. This paper carefully surveyed the previous work on rule extraction from neural network ensembles since 1988. We are aware of three major research groups i.e., Bologna' group, Zhou' group and Hayashi' group. The reason of these situations is obvious. Since the structures of previous neural network ensembles were quite complicated, the research on the efficient rule extraction algorithm from neural network ensembles was few although their learning capability was extremely high. Thus, these issues make rule extraction algorithm for neural network ensemble difficult task. However, there is a practical need for new ideas for neural network ensembles in order to realize the extremely high-performance needs of various rule extraction problems in real life. This paper successively explain nature of artificial neural networks, origin of neural network rule extraction, incorporating fuzziness in neural network rule extraction, theoretical foundation of neural network rule extraction, computational complexity of neural network rule extraction, neuro-fuzzy hybridization, previous rule extraction from neural network ensembles and difficulties of previous neural network ensembles. Next, this paper address three principles of proposed neural network rule extraction: to increase recognition rates, to extract rules from neural network ensembles, and to minimize the use of computing resources. We also propose an ensemble-recursive-rule extraction (E-Re-RX) by two or three standard backpropagation to train multi-layer perceptrons (MLPs), which enabled extremely high recognition accuracy and the extraction of comprehensible rules. Furthermore, this enabled rule extraction that resulted in fewer rules than those in previously proposed methods. This paper summarizes experimental results of rule extraction using E-Re-RX by multiple standard backpropagation MLPs and provides deep discussions. The results make it possible for the output from a neural network ensemble to be in the form of rules, thus open the "black box" of trained neural networks ensembles. Finally, we provide valuable conclusions and as future work, three open questions on the E-Re-RX algorithm.

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Zhang, Shiqing, Yueli Cui, Yuelong Chuang, Wenping Guo, Ying Chen, and Xiaoming Zhao. "Spoken Emotion Recognition by Combining Deep Belief Networks and Multi-layer Perceptron." International Journal of Multimedia and Ubiquitous Engineering 12, no. 2 (2017): 107–16. http://dx.doi.org/10.14257/ijmue.2017.12.2.08.

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Souza Filho, João B. O., and José Manoel Seixas. "Class‐modular multi‐layer perceptron networks for supporting passive sonar signal classification." IET Radar, Sonar & Navigation 10, no. 2 (2016): 311–17. http://dx.doi.org/10.1049/iet-rsn.2015.0179.

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Zhao, Zongyuan, Shuxiang Xu, Byeong Ho Kang, Mir Md Jahangir Kabir, Yunling Liu, and Rainer Wasinger. "Investigation and improvement of multi-layer perceptron neural networks for credit scoring." Expert Systems with Applications 42, no. 7 (2015): 3508–16. http://dx.doi.org/10.1016/j.eswa.2014.12.006.

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Tur, Rifat, and Serbay Yontem. "A Comparison of Soft Computing Methods for the Prediction of Wave Height Parameters." Knowledge-Based Engineering and Sciences 2, no. 1 (2021): 31–46. http://dx.doi.org/10.51526/kbes.2021.2.1.31-46.

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In the previous studies on the prediction of wave height parameters, only the significant wave height has been considered as the unknown parameter to be predicted. However, the other wave height parameters, which may be required for the design of coastal structures depending on their importance level, have been neglected. Therefore, in this study, novel soft computing methods were used to predict all wave height parameters required for the design of coastal structures. To this end, wave data were derived from a buoy located in Southwest Black Sea Coast. Then, Multi-layer Perceptron Neural Network (MLPNN) and Adaptive-Neuro Fuzzy Inference System (ANFIS) models were developed to predict wave height parameters. Various input combinations were selected to create seven different sub-models. These sub-models were applied using developed MLPNN and ANFIS models. Accuracy of sub-models were evaluated for each wave height parameters in terms of performance evaluation criteria. The results showed that the wave height parameters predicted by the MLPNN and ANFIS methods are similar and both methods yield results acceptable for design purposes. However, for maximum wave height, Hmax, ANFIS sub-model yields slightly better results.

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Rediniotis, O. K., and G. Chrysanthakopoulos. "Application of Neural Networks and Fuzzy Logic to the Calibration of the Seven-Hole Probe." Journal of Fluids Engineering 120, no. 1 (1998): 95–101. http://dx.doi.org/10.1115/1.2819670.

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The theory and techniques of Artificial Neural Networks (ANN) and Fuzzy Logic Systems (FLS) are applied toward the formulation of accurate and wide-range calibration methods for such flow-diagnostics instruments as multi-hole probes. Besides introducing new calibration techniques, part of the work’s objective is to: (a) apply fuzzy-logic methods to identify systems whose behavior is described in a “crisp” rather than a “linguistic” framework and (b) compare the two approaches, i.e., neural network versus fuzzy logic approach, and their potential as universal approximators. For the ANN approach, several network configurations were tried. A Multi-Layer Perceptron with a 2-node input layer, a 4-node output layer and a 7-node hidden/middle layer, performed the best. For the FLS approach, a system with center average defuzzifier, product-inference rule, singleton fuzzifier, and Gaussian membership functions was employed. The Fuzzy Logic System seemed to outperform the Neural Network/Multi-Layer Perceptron.

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Aguilar-Fuertes, Jose J., Francisco Noguero-Rodríguez, José C. Jaen Ruiz, Luis M. García-RAffi, and Sergio Hoyas. "Tracking Turbulent Coherent Structures by Means of Neural Networks." Energies 14, no. 4 (2021): 984. http://dx.doi.org/10.3390/en14040984.

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The behaviours of individual flow structures have become a relevant matter of study in turbulent flows as the computational power to allow their study feasible has become available. Especially, high instantaneous Reynolds Stress events have been found to dominate the behaviour of the logarithmic layer. In this work, we present a viability study where two machine learning solutions are proposed to reduce the computational cost of tracking such structures in large domains. The first one is a Multi-Layer Perceptron. The second one uses Long Short-Term Memory (LSTM). Both of the methods are developed with the objective of taking the the structures’ geometrical features as inputs from which to predict the structures’ geometrical features in future time steps. Some of the tested Multi-Layer Perceptron architectures proved to perform better and achieve higher accuracy than the LSTM architectures tested, providing lower errors on the predictions and achieving higher accuracy in relating the structures in the consecutive time steps.

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Sutton, R., C. Johnson, and G. N. Roberts. "A Neural Auto-depth Controller for an Unmanned Underwater Vehicle." Journal of Navigation 50, no. 2 (1997): 292–302. http://dx.doi.org/10.1017/s0373463300023912.

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Artificial neural networks offer an alternative strategy for the nonlinear control of unmanned underwater vehicles (UUVS). This paper investigates the use of a multi-layered perceptron (MLP) network in controlling an UUV over a sea-bed profile and compares the use of applying chemotaxis learning to that of the more commonly employed back propagation algorithm. The results show that, for differing sized MLPs, the chemotaxis algorithm produces a successful controller over the sea-bed profile in an improved training time. Also it will be shown that, in the presence of noise and change in vehicle mass, the neural controller out-performed a classical proportional-integral-derivative controller.

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Malik, Anurag, Anil Kumar, Priya Rai, and Alban Kuriqi. "Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models." Climate 9, no. 2 (2021): 28. http://dx.doi.org/10.3390/cli9020028.

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Accurate monitoring and forecasting of drought are crucial. They play a vital role in the optimal functioning of irrigation systems, risk management, drought readiness, and alleviation. In this work, Artificial Intelligence (AI) models, comprising Multi-layer Perceptron Neural Network (MLPNN) and Co-Active Neuro-Fuzzy Inference System (CANFIS), and regression, model including Multiple Linear Regression (MLR), were investigated for multi-scalar Standardized Precipitation Index (SPI) prediction in the Garhwal region of Uttarakhand State, India. The SPI was computed on six different scales, i.e., 1-, 3-, 6-, 9-, 12-, and 24-month, by deploying monthly rainfall information of available years. The significant lags as inputs for the MLPNN, CANFIS, and MLR models were obtained by utilizing Partial Autocorrelation Function (PACF) with a significant level equal to 5% for SPI-1, SPI-3, SPI-6, SPI-9, SPI-12, and SPI-24. The predicted multi-scalar SPI values utilizing the MLPNN, CANFIS, and MLR models were compared with calculated SPI of multi-time scales through different performance evaluation indicators and visual interpretation. The appraisals of results indicated that CANFIS performance was more reliable for drought prediction at Dehradun (3-, 6-, 9-, and 12-month scales), Chamoli and Tehri Garhwal (1-, 3-, 6-, 9-, and 12-month scales), Haridwar and Pauri Garhwal (1-, 3-, 6-, and 9-month scales), Rudraprayag (1-, 3-, and 6-month scales), and Uttarkashi (3-month scale) stations. The MLPNN model was best at Dehradun (1- and 24- month scales), Tehri Garhwal and Chamoli (24-month scale), Haridwar (12- and 24-month scales), Pauri Garhwal (12-month scale), Rudraprayag (9-, 12-, and 24-month), and Uttarkashi (1- and 6-month scales) stations, while the MLR model was found to be optimal at Pauri Garhwal (24-month scale) and Uttarkashi (9-, 12-, and 24-month scales) stations. Furthermore, the modeling approach can foster a straightforward and trustworthy expert intelligent mechanism for projecting multi-scalar SPI and decision making for remedial arrangements to tackle meteorological drought at the stations under study.

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Le, Thai Hoang. "Applying Artificial Neural Networks for Face Recognition." Advances in Artificial Neural Systems 2011 (November 3, 2011): 1–16. http://dx.doi.org/10.1155/2011/673016.

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This paper introduces some novel models for all steps of a face recognition system. In the step of face detection, we propose a hybrid model combining AdaBoost and Artificial Neural Network (ABANN) to solve the process efficiently. In the next step, labeled faces detected by ABANN will be aligned by Active Shape Model and Multi Layer Perceptron. In this alignment step, we propose a new 2D local texture model based on Multi Layer Perceptron. The classifier of the model significantly improves the accuracy and the robustness of local searching on faces with expression variation and ambiguous contours. In the feature extraction step, we describe a methodology for improving the efficiency by the association of two methods: geometric feature based method and Independent Component Analysis method. In the face matching step, we apply a model combining many Neural Networks for matching geometric features of human face. The model links many Neural Networks together, so we call it Multi Artificial Neural Network. MIT + CMU database is used for evaluating our proposed methods for face detection and alignment. Finally, the experimental results of all steps on CallTech database show the feasibility of our proposed model.

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Tamim, Nasser, M. Elshrkawey, Gamil Abdel Azim, and Hamed Nassar. "Retinal Blood Vessel Segmentation Using Hybrid Features and Multi-Layer Perceptron Neural Networks." Symmetry 12, no. 6 (2020): 894. http://dx.doi.org/10.3390/sym12060894.

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Segmentation of retinal blood vessels is the first step for several computer aided-diagnosis systems (CAD), not only for ocular disease diagnosis such as diabetic retinopathy (DR) but also of non-ocular disease, such as hypertension, stroke and cardiovascular diseases. In this paper, a supervised learning-based method, using a multi-layer perceptron neural network and carefully selected vector of features, is proposed. In particular, for each pixel of a retinal fundus image, we construct a 24-D feature vector, encoding information on the local intensity, morphology transformation, principal moments of phase congruency, Hessian, and difference of Gaussian values. A post-processing technique depending on mathematical morphological operators is used to optimise the segmentation. Moreover, the selected feature vector succeeded in outfitting the symmetric features that provided the final blood vessel probability as a binary map image. The proposed method is tested on three known datasets: Digital Retinal Image for Extraction (DRIVE), Structure Analysis of the Retina (STARE), and CHASED_DB1 datasets. The experimental results, both visual and quantitative, testify to the robustness of the proposed method. This proposed method achieved 0.9607, 0.7542, and 0.9843 in DRIVE, 0.9632, 0.7806, and 0.9825 on STARE, 0.9577, 0.7585 and 0.9846 in CHASE_DB1, with respectable accuracy, sensitivity, and specificity performance metrics. Furthermore, they testify that the method is superior to seven similar state-of-the-art methods.

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Harzallah, Salaheddine, R. Rebhi, M. Chabaat, and A. Rabehi. "Eddy current modelling using multi-layer perceptron neural networks for detecting surface cracks." Frattura ed Integrità Strutturale 12, no. 45 (2018): 147–55. http://dx.doi.org/10.3221/igf-esis.45.12.

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Journal articles on the topic 'Multi-layer perceptron networks (MLPNs)'

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