Relevant bibliographies by topics / Feed Forward Neural Network (FFNN)

Academic literature on the topic 'Feed Forward Neural Network (FFNN)'

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Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Feed Forward Neural Network (FFNN)"

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Hasbi, Yasin, Warsito Budi, and Santoso Rukun. "Feed Forward Neural Network Modeling for Rainfall Prediction." E3S Web of Conferences 73 (2018): 05017. http://dx.doi.org/10.1051/e3sconf/20187305017.

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Prediction of rainfall data by using Feed Forward Neural Network (FFNN) model is proposed. FFNN is a class of neural network which has three layers for processing. In time series prediction, including in case of rainfall data, the input layer is the past values of the same series up to certain lag and the output layer is the current value. Beside a few lagged times, the seasonal pattern also considered as an important aspect of choosing the potential input. The autocorrelation function and partial autocorrelation function patterns are used as aid of selecting the input. In the second layer called hidden layer, the logistic sigmoid is used as activation function because of the monotonic and differentiable. Processing is done by the weighted summing of the input variables and transfer process in the hidden layer. Backpropagation algorithm is applied in the training process. Some gradient based optimization methods are used to obtain the connection weights of FFNN model. The prediction is the output resulting of the process in the last layer. In each optimization method, the looping process is performed several times in order to get the most suitable result in various composition of separating data. The best one is chosen by the least mean square error (MSE) criteria. The least of in-sample and out-sample predictions from the repeating results been the base of choosing the best optimization method. In this study, the model is applied in the ten-daily rainfall data of ZOM 136 Cokrotulung Klaten. Simulation results give a consecution that the more complex architecture is not guarantee the better prediction.
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Aribowo, Widi, Supari Muslim, Fendi Achmad, and Aditya Chandra Hermawan. "Improving Neural Network Based on Seagull Optimization Algorithm for Controlling DC Motor." Jurnal Elektronika dan Telekomunikasi 21, no. 1 (August 31, 2021): 48. http://dx.doi.org/10.14203/jet.v21.48-54.

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This article presents a direct current (DC) motor control approach using a hybrid Seagull Optimization Algorithm (SOA) and Neural Network (NN) method. SOA method is a nature-inspired algorithm. DC motor speed control is very important to maintain the stability of motor operation. The SOA method is an algorithm that duplicates the life of the seagull in nature. Neural network algorithms will be improved using the SOA method. The neural network used in this study is a feed-forward neural network (FFNN). This research will focus on controlling DC motor speed. The efficacy of the proposed method is compared with the Proportional Integral Derivative (PID) method, the Feed Forward Neural Network (FFNN), and the Cascade Forward Backpropagation Neural Network (CFBNN). From the results of the study, the proposed control method has good capabilities compared to standard neural methods, namely FFNN and CFBNN. Integral Time Absolute Error and Square Error (ITAE and ITSE) values from the proposed method are on average of 0.96% and 0.2% better than the FFNN and CFBNN methods.
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Aldakheel, Fadi, Ramish Satari, and Peter Wriggers. "Feed-Forward Neural Networks for Failure Mechanics Problems." Applied Sciences 11, no. 14 (July 14, 2021): 6483. http://dx.doi.org/10.3390/app11146483.

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This work addresses an efficient neural network (NN) representation for the phase-field modeling of isotropic brittle fracture. In recent years, data-driven approaches, such as neural networks, have become an active research field in mechanics. In this contribution, deep neural networks—in particular, the feed-forward neural network (FFNN)—are utilized directly for the development of the failure model. The verification and generalization of the trained models for elasticity as well as fracture behavior are investigated by several representative numerical examples under different loading conditions. As an outcome, promising results close to the exact solutions are produced.
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Dwi Prasetyo, Mohammad Imron, Anang Tjahjono, and Novie Ayub Windarko. "FEED FORWARD NEURAL NETWORK SEBAGAI ALGORITMA ESTIMASI STATE OF CHARGE BATERAI LITHIUM POLYMER." KLIK - KUMPULAN JURNAL ILMU KOMPUTER 7, no. 1 (March 2, 2020): 13. http://dx.doi.org/10.20527/klik.v7i1.290.

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<p><em>Estimasi State Of Charge (SOC) baterai merupakan parameter terpenting dalam Battery Management System (BMS), terlebih sebagai aplikasi dari mobil listrik dan smart grid. SOC tidak dapat dilakukan pengukuran secara langsung, sehingga diperlukan metode estimasi untuk mendapatkan nilai tersebut. Beberapa metode yang pernah diusulkan adalah coloumb counting dan open circuit voltage. Akan tetapi coloumb counting memiliki kelemahan dalam hal inisialisasi SOC awal dan memiliki ketergantungan terhadap sensor arus. Sedangkan metode open circuit voltage hanya dapat digunakan pada baterai dalam kondisi idel. Pada penelitian ini diusulkan metode algoritma Feed Forward Neural Network (FFNN) untuk estimasi SOC baterai lithium polymer. Algoritma ini dapat menyelesaikan sistem nonlinier seperti yang dimiliki oleh baterai lithium polymer. Arsitektur FFNN dibangun dua kali (dual neural) untuk estimasi OCV dan SOC. FFNN pertama dengan input tegangan, arus, dan waktu charging maupun discharging untuk estimasi OCV. OCV hasil training neural pertama digunakan sebagai input FFNN kedua untuk estimasi SOC. Hasil dari estimasi ini didapatkan dengan nilai hidden neuron 11 pada neural pertama dan hidden neuron 4 pada neural kedua.</em></p><p><strong>Keywords:</strong><em> </em><em>SOC, BMS, Coloumb Counting, OCV, FFNN</em></p><p><em>Estimasi State Of Charge (SOC) baterai merupakan parameter terpenting dalam Battery Management System (BMS), terlebih sebagai aplikasi dari mobil listrik dan smart grid. SOC tidak dapat dilakukan pengukuran secara langsung, sehingga diperlukan metode estimasi untuk mendapatkan nilai tersebut. Beberapa metode yang pernah diusulkan adalah coloumb counting dan open circuit voltage. Akan tetapi coloumb counting memiliki kelemahan dalam hal inisialisasi SOC awal dan memiliki ketergantungan terhadap sensor arus. Sedangkan metode open circuit voltage hanya dapat digunakan pada baterai dalam kondisi idel. Pada penelitian ini diusulkan metode algoritma Feed Forward Neural Network (FFNN) untuk estimasi SOC baterai lithium polymer. Algoritma ini dapat menyelesaikan sistem nonlinier seperti yang dimiliki oleh baterai lithium polymer. Arsitektur FFNN dibangun dua kali (dual neural) untuk estimasi OCV dan SOC. FFNN pertama dengan input tegangan, arus, dan waktu charging maupun discharging untuk estimasi OCV. OCV hasil training neural pertama digunakan sebagai input FFNN kedua untuk estimasi SOC. Hasil dari estimasi ini didapatkan dengan nilai hidden neuron 11 pada neural pertama dan hidden neuron 4 pada neural kedua.</em></p><p><strong>Kata kunci</strong><em>: </em><em>SOC, BMS, Coloumb Counting, OCV, FFNN</em></p><p><em><br /></em></p><p><em><br /></em></p>
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S K. Dhakad, S. K. Dhakad, Dr S. C. soni Dr. S.C.soni, and Dr Pankaj Agrawal. "The feed forward neural network (FFNN) based model prediction of Molten Carbonate Fuel cells (MCFCs)." Indian Journal of Applied Research 3, no. 2 (October 1, 2011): 142–43. http://dx.doi.org/10.15373/2249555x/feb2013/49.

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Novickis, Rihards, Daniels Jānis Justs, Kaspars Ozols, and Modris Greitāns. "An Approach of Feed-Forward Neural Network Throughput-Optimized Implementation in FPGA." Electronics 9, no. 12 (December 18, 2020): 2193. http://dx.doi.org/10.3390/electronics9122193.

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Artificial Neural Networks (ANNs) have become an accepted approach for a wide range of challenges. Meanwhile, the advancement of chip manufacturing processes is approaching saturation which calls for new computing solutions. This work presents a novel approach of an FPGA-based accelerator development for fully connected feed-forward neural networks (FFNNs). A specialized tool was developed to facilitate different implementations, which splits FFNN into elementary layers, allocates computational resources and generates high-level C++ description for high-level synthesis (HLS) tools. Various topologies are implemented and benchmarked, and a comparison with related work is provided. The proposed methodology is applied for the implementation of high-throughput virtual sensor.
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Zainudin, Fathin Liyana, Sharifah Saon, Abd Kadir Mahamad, Musli Nizam Yahya, Mohd Anuaruddin Ahmadon, and Shingo Yamaguchi. "Feed forward neural network application for classroom reverberation time estimation." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 1 (July 1, 2019): 346. http://dx.doi.org/10.11591/ijeecs.v15.i1.pp346-354.

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<span>Acoustic problem is a main issues of the existing classroom due to lack of absorption of surface material. Thus, a feed forward neural network system (FFNN) for classroom Reverberation Time (RT) estimation computation was built. This system was developed to assist the acoustic engineer and consultant to treat and reduce this matter. Data was collected and computed using ODEON12.10 ray tracing method, resulting in a total of 600 rectangular shaped classroom models that were modeled with various length, width, height, as well as different surface material types. The system is able to estimate RT for 500Hz, 1000Hz, and 2000Hz. Using the collected data, FFNN for each frequency were trained and simulated separately (as absorption coefficients are frequency dependent) in order to find the optimum solution. The final system was validated and compared with the actual measurement value from 15 different classrooms in Universiti Tun Hussein Onn Malaysia (UTHM). The developed system show positive results with average validation accuracy of 94.35%, 95.91%, and 96.42% for 500Hz, 1000Hz, and 2000Hz respectively. </span>
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Aribowo, Widi, Bambang Suprianto, and Joko Joko. "Improving neural network using a sine tree-seed algorithm for tuning motor DC." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 2 (June 1, 2021): 1196. http://dx.doi.org/10.11591/ijpeds.v12.i2.pp1196-1204.

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A DC motor is applied to delicate speed and position in the industry. The stability and productivity of a system are keys for tuning of a DC motor speed. Stabilized speed is influenced by load sway and environmental factors. In this paper, a comparison study in diverse techniques to tune the speed of the DC motor with parameter uncertainties is showed. The research has discussed the application of the feed-forward neural network (FFNN) which is enhanced by a sine tree-seed algorithm (STSA). STSA is a hybrid method of the tree-seed algorithm (TSA) and Sine Cosine algorithm. The STSA method is aimed to improve TSA performance based on the sine cosine algorithm (SCA) method. A feed-forward neural network (FFNN) is popular and capable of nonlinear issues. The focus of the research is on the achievement speed of DC motor. In addition, the proposed method will be compared with proportional integral derivative (PID), FFNN, marine predator algorithm-feed-forward neural network (MPA-NN) and atom search algorithm-feed-forward neural network (ASO-NN). The performance of the speed from the proposed method has the best result. The settling time value of the proposed method is more stable than the PID method. The ITAE value of the STSA-NN method was 31.3% better than the PID method. Meanwhile, the ITSE value is 29.2% better than the PID method.
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Sallam, Tarek, Ahmed Attiya, and Nada El-Latif. "Neural-Network-Based Multiobjective Optimizer for Dual-Band Circularly Polarized Antenna." Applied Computational Electromagnetics Society 36, no. 3 (April 20, 2021): 252–58. http://dx.doi.org/10.47037/2020.aces.j.360304.

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A multiobjective optimization (MOO) technique for a dual-band circularly polarized antenna by using neural networks (NNs) is introduced in this paper. In particular, the optimum antenna dimensions are computed by modeling the problem as a multilayer feed-forward neural network (FFNN), which is two-stage trained with I/O pairs. The FFNN is chosen because of its characteristic of accurate approximation and good generalization. The data for FFNN training is obtained by using HFSS EM simulator by varying different geometrical parameters of the antenna. A two strip-loaded circular aperture antenna is utilized to demonstrate the optimization technique. The target dual bands are 835–865 MHz and 2.3–2.35 GHz.
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Cloud, Kirkwood A., Brian J. Reich, Christopher M. Rozoff, Stefano Alessandrini, William E. Lewis, and Luca Delle Monache. "A Feed Forward Neural Network Based on Model Output Statistics for Short-Term Hurricane Intensity Prediction." Weather and Forecasting 34, no. 4 (July 24, 2019): 985–97. http://dx.doi.org/10.1175/waf-d-18-0173.1.

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Abstract A feed forward neural network (FFNN) is developed for tropical cyclone (TC) intensity prediction, where intensity is defined as the maximum 1-min average 10-m wind speed. This deep learning model incorporates a real-time operational estimate of the current intensity and predictors derived from Hurricane Weather Research and Forecasting (HWRF; 2017 version) Model forecasts. The FFNN model is developed with the operational constraint of being restricted to 6-h-old HWRF data. Best track intensity data are used for observational verification. The forecast training data are from 2014 to 2016 HWRF reforecast data and cover a wide variety of TCs from both the Atlantic and eastern Pacific Ocean basins. Cross validation shows that the FFNN increasingly outperforms the operational observation-adjusted HWRF (HWFI) in terms of mean absolute error (MAE) at forecast lead times from 3 to 57 h. Out-of-sample testing on real-time data from 2017 shows the HWFI produces lower MAE than the FFNN at lead times of 24 h or less and similar MAEs at later lead times. On the other hand, the 2017 data indicate significant potential for the FFNN in the prediction of rapid intensification (RI), with RI defined here as an intensification of at least 30 kt (1 kt ≈ 0.51 m s−1) in a 24-h period. The FFNN produces 4 times the number of hits in HWFI for RI. While the FFNN has more false alarms than the HWFI, Brier skill scores show that, in the Atlantic, the FFNN has significantly greater skill than the HWFI and probabilistic Statistical Hurricane Intensity Prediction System RI index.
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Dissertations / Theses on the topic "Feed Forward Neural Network (FFNN)"

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Khanna, Neha, and Neha Khanna@mdbc gov au. "Investigation of phytoplankton dynamics using time-series analysis of biophysical parameters in Gippsland Lakes, South-eastern Australia." RMIT University. Civil, Environmental and Chemical Engineering, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080226.123435.

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There is a need for ecological modelling to help understand the dynamics in ecological systems, and thus aid management decisions to maintain or improve the quality of the ecological systems. This research focuses on non linear statistical modelling of observations from an estuarine system, Gippsland Lakes, on the south-eastern coast of Australia. Feed forward neural networks are used to model chlorophyll time series from a fixed monitoring station at Point King. The research proposes a systematic approach to modelling in ecology using feed forward neural networks, to ensure: (a) that results are reliable, (b) to improve the understanding of dynamics in the ecological system, and (c) to obtain a prediction, if possible. An objective filtering algorithm to enable modelling is presented. Sensitivity analysis techniques are compared to select the most appropriate technique for ecological models. The research generated a chronological profile of relationships between biophysical parameters and chlorophyll level for different seasons. A sensitivity analysis of the models was used to understand how the significance of the biophysical parameters changes as the time difference between the input and predicted value changes. The results show that filtering improves modelling without introducing any noticeable bias. Partial derivative method is found to be the most appropriate technique for sensitivity analysis of ecological feed forward neural networks models. Feed forward neural networks show potential for prediction when modelled on an appropriate time series. Feed forward neural networks also show capability to increase understanding of the ecological environment. In this research, it can be seen that vertical gradient and temperature are important for chlorophyll levels at Point King at time scales from a few hours to a few days. The importance of chlorophyll level at any time to chlorophyll levels in the future reduces as the time difference between them increases.
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Hadjiprocopis, Andreas. "Feed forward neural network entities." Thesis, City University London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340374.

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Tanaka, Toshiyuki. "Control of growth dynamics of feed-forward neural network." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/13445.

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Al-Mudhaf, Ali F. "A feed forward neural network approach for matrix computations." Thesis, Brunel University, 2001. http://bura.brunel.ac.uk/handle/2438/5010.

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A new neural network approach for performing matrix computations is presented. The idea of this approach is to construct a feed-forward neural network (FNN) and then train it by matching a desired set of patterns. The solution of the problem is the converged weight of the FNN. Accordingly, unlike the conventional FNN research that concentrates on external properties (mappings) of the networks, this study concentrates on the internal properties (weights) of the network. The present network is linear and its weights are usually strongly constrained; hence, complicated overlapped network needs to be construct. It should be noticed, however, that the present approach depends highly on the training algorithm of the FNN. Unfortunately, the available training methods; such as, the original Back-propagation (BP) algorithm, encounter many deficiencies when applied to matrix algebra problems; e. g., slow convergence due to improper choice of learning rates (LR). Thus, this study will focus on the development of new efficient and accurate FNN training methods. One improvement suggested to alleviate the problem of LR choice is the use of a line search with steepest descent method; namely, bracketing with golden section method. This provides an optimal LR as training progresses. Another improvement proposed in this study is the use of conjugate gradient (CG) methods to speed up the training process of the neural network. The computational feasibility of these methods is assessed on two matrix problems; namely, the LU-decomposition of both band and square ill-conditioned unsymmetric matrices and the inversion of square ill-conditioned unsymmetric matrices. In this study, two performance indexes have been considered; namely, learning speed and convergence accuracy. Extensive computer simulations have been carried out using the following training methods: steepest descent with line search (SDLS) method, conventional back propagation (BP) algorithm, and conjugate gradient (CG) methods; specifically, Fletcher Reeves conjugate gradient (CGFR) method and Polak Ribiere conjugate gradient (CGPR) method. The performance comparisons between these minimization methods have demonstrated that the CG training methods give better convergence accuracy and are by far the superior with respect to learning time; they offer speed-ups of anything between 3 and 4 over SDLS depending on the severity of the error goal chosen and the size of the problem. Furthermore, when using Powell's restart criteria with the CG methods, the problem of wrong convergence directions usually encountered in pure CG learning methods is alleviated. In general, CG methods with restarts have shown the best performance among all other methods in training the FNN for LU-decomposition and matrix inversion. Consequently, it is concluded that CG methods are good candidates for training FNN of matrix computations, in particular, Polak-Ribidre conjugate gradient method with Powell's restart criteria.
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Richards, Gareth D. "Implementation and capabilities of layered feed-forward networks." Thesis, University of Edinburgh, 1990. http://hdl.handle.net/1842/11313.

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Mohammadi, Mohammad Mehdi. "PREDICTION OF WIND TURBINE BLADE FATIGUE LOADS USING FEED-FORWARD NEURAL NETWORKS." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-444115.

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In recent years, machine learning applications have gained great attention in the wind power industry. Among these, artificial neural networks have been utilized to predict the fatigue loads of wind turbine components such as rotor blades. However, the limited number of contributions and differences in the used databases give rise to several questions which this study has aimed to answer. Therefore, in this study, 5-min SCADA data from the Lillgrund wind farm has been used to train two feed-forward neural networks to predict the fatigue loads at the blade root in flapwise and edgewise directions in the shape of damage equivalent loads.The contribution of different features to the model’s performance is evaluated. In the absence of met mast measurements, mesoscale NEWA data are utilized to present the free flow condition. Also, the effect of wake condition on the model’s accuracy is examined. Besides, the generalization ability of the model trained on data points from one or multiple turbines on other turbines within the farm is investigated. The results show that the best accuracy was achieved for a model with 34 features, 5 hidden layers with 100 neurons in each hidden layer for the flapwise direction. For the edgewise direction, the best model has 54 features, 6 hidden layers, and 125 neurons in each hidden layer.For a model trained and tested on the same turbine, mean absolute percentage errors (MAPE) of 0.78% and 9.31% are achieved for the flapwise and edgewise directions, respectively. The seen difference is argued to be a result of not having enough data points throughout the range of edgewise moments. The use of NEWA data has been shown to improve the model’s accuracy by 10% for MAPE values, relatively. Training the model under different wake conditions did not improve the model showing that the wake effects are captured through the input features to some extent. Generalization of the model trained on data points from one turbine resulted in poor results in the flapwise direction. It was shown that using data points from multiple turbines can improve the model’s accuracy to predict loading on other turbines.
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Nyman, Jacob. "Machinery Health Indicator Construction using Multi-objective Genetic Algorithm Optimization of a Feed-forward Neural Network based on Distance." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298084.

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Assessment of machine health and prediction of future failures are critical for maintenance decisions. Many of the existing methods use unsupervised techniques to construct health indicators by measuring the disparity between the current state and either the healthy or the faulty states of the system. This approach can work well, but if the resulting health indicators are insufficient there is no easy way to steer the algorithm towards better ones. In this thesis a new method for health indicator construction is investigated that aims to solve this issue. It is based on measuring distance after transforming the sensor data into a new space using a feed-forward neural network. The feed-forward neural network is trained using a multi-objective optimization algorithm, NSGA-II, to optimize criteria that are desired in a health indicator. Thereafter the constructed health indicator is passed into a gated recurrent unit for remaining useful life prediction. The approach is compared to benchmarks on the NASA Turbofan Engine Degradation Simulation dataset and in regard to the size of the neural networks, the model performs relatively well, but does not outperform the results reported by a few of the more recent methods. The method is also investigated on a simulated dataset based on elevator weights with two independent failures. The method is able to construct a single health indicator with a desirable shape for both failures, although the latter estimates of time until failure are overestimated for the more rare failure type. On both datasets the health indicator construction method is compared with a baseline without transformation function and does in both cases outperform it in terms of the resulting remaining useful life prediction error using the gated recurrent unit. Overall, the method is shown to be flexible in generating health indicators with different characteristics and because of its properties it is adaptive to different remaining useful life prediction methods.
Estimering av maskinhälsa och prognos av framtida fel är kritiska steg för underhållsbeslut. Många av de befintliga metoderna använder icke-väglett (unsupervised) lärande för att konstruera hälsoindikatorer som beskriver maskinens tillstånd över tid. Detta sker genom att mäta olikheter mellan det nuvarande tillståndet och antingen de friska eller fallerande tillstånden i systemet. Det här tillvägagångssättet kan fungera väl, men om de resulterande hälsoindikatorerna är otillräckliga så finns det inget enkelt sätt att styra algoritmen mot bättre. I det här examensarbetet undersöks en ny metod för konstruktion av hälsoindikatorer som försöker lösa det här problemet. Den är baserad på avståndsmätning efter att ha transformerat indatat till ett nytt vektorrum genom ett feed-forward neuralt nätverk. Nätverket är tränat genom en multi-objektiv optimeringsalgoritm, NSGA-II, för att optimera kriterier som är önskvärda hos en hälsoindikator. Därefter används den konstruerade hälsoindikatorn som indata till en gated recurrent unit (ett neuralt nätverk som hanterar sekventiell data) för att förutspå återstående livslängd hos systemet i fråga. Metoden jämförs med andra metoder på ett dataset från NASA som simulerar degradering hos turbofan-motorer. Med avseende på storleken på de använda neurala nätverken så är resultatet relativt bra, men överträffar inte resultaten rapporterade från några av de senaste metoderna. Metoden testas även på ett simulerat dataset baserat på elevatorer som fraktar säd med två oberoende fel. Metoden lyckas skapa en hälsoindikator som har en önskvärd form för båda felen. Dock så överskattar den senare modellen, som använde hälsoindikatorn, återstående livslängd vid estimering av det mer ovanliga felet. På båda dataseten jämförs metoden för hälsoindikatorkonstruktion med en basmetod utan transformering, d.v.s. avståndet mäts direkt från grund-datat. I båda fallen överträffar den föreslagna metoden basmetoden i termer av förutsägelsefel av återstående livslängd genom gated recurrent unit- nätverket. På det stora hela så visar sig metoden vara flexibel i skapandet av hälsoindikatorer med olika attribut och p.g.a. metodens egenskaper är den adaptiv för olika typer av metoder som förutspår återstående livslängd.
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Nigrini, L. B., and G. D. Jordaan. "Short term load forecasting using neural networks." Journal for New Generation Sciences, Vol 11, Issue 3: Central University of Technology, Free State, Bloemfontein, 2013. http://hdl.handle.net/11462/646.

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Published Article
Several forecasting models are available for research in predicting the shape of electric load curves. The development of Artificial Intelligence (AI), especially Artificial Neural Networks (ANN), can be applied to model short term load forecasting. Because of their input-output mapping ability, ANN's are well-suited for load forecasting applications. ANN's have been used extensively as time series predictors; these can include feed-forward networks that make use of a sliding window over the input data sequence. Using a combination of a time series and a neural network prediction method, the past events of the load data can be explored and used to train a neural network to predict the next load point. In this study, an investigation into the use of ANN's for short term load forecasting for Bloemfontein, Free State has been conducted with the MATLAB Neural Network Toolbox where ANN capabilities in load forecasting, with the use of only load history as input values, are demonstrated.
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Karlsson, Nils. "Comparison of linear regression and neural networks for stock price prediction." Thesis, Uppsala universitet, Signaler och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445237.

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Stock market prediction has been a hot topic lately due to advances in computer technology and economics. One economic theory, called Efficient Market Hypothesis (EMH), states that all known information is already factored into the prices which makes it impossible to predict the stock market. Despite the EMH, many researchers have been successful in predicting the stock market using neural networks on historical data. This thesis investigates stock prediction using both linear regression and neural networks (NN), with a twist. The inputs to the proposed methods are a number of profit predictions calculated with stochastic methods such as generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive integrated moving average (ARIMA). By contrast the traditional approach was instead to use raw data as inputs. The proposed methods show superior result in yielding profit: at best 1.1% in the Swedish market and 4.6% in the American market. The neural network yielded more profit than the linear regression model, which is reasonable given its ability to find nonlinear patterns. The historical data was used with different window sizes. This gives a good understanding of the window size impact on the prediction performance.
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Gróf, Zoltán. "Realizace rozdělujících nadploch." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219781.

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The main aim of this master's thesis is to describe the subject of the implementation of decision boundaries with the help of artificial neural networks. The objective is to present theoretical knowledge concerning this field and on practical examples prove these statements. The work contains basic theoretical description of the field of pattern recognition and the field of feature based representation of objects. A classificator working on the basis of Bayes decision is presented in this part, and other types of classificators are named as well. The work then deals with artificial neural networks in more detail; it contains a theoretical description of their function and their abilities in the creation of decision boundaries in the feature plane. Examples are shown from literature for the use of neural networks in corresponding problems. As part of this work, the program ANN-DeBC was created using Matlab, for the generation of practical results about the usage of feed-forward neural networks for the implementation of decision boundaries. The work contains a detailed description of this program, and the achieved results are presented and analyzed. It is shown as well, how artificial neural networks are creating decision boundaries in the form of geometrical shapes. The effects of the chosen topology of the neural network and the number of training samples on the success of the classification are observed, and the minimal values of these parameters are determined for the successful creation of decision boundaries at the individual examples. Furthermore, it's presented how the neural networks behave at the classification of realistically distributed training samples, and what methods can affect the shape of the created decision boundaries.
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Book chapters on the topic "Feed Forward Neural Network (FFNN)"

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Kingdon, Jason. "Feed-Forward Neural Network Modelling." In Perspectives in Neural Computing, 37–53. London: Springer London, 1997. http://dx.doi.org/10.1007/978-1-4471-0949-5_3.

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Hadjiprocopis, Andreas, and Peter Smith. "Feed Forward Neural Network entities." In Biological and Artificial Computation: From Neuroscience to Technology, 349–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0032493.

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Sher, Gene I. "Developing a Feed Forward Neural Network." In Handbook of Neuroevolution Through Erlang, 153–85. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4463-3_6.

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Ferrán, Edgardo A., and Roberto P. J. Perazzo. "Symmetry and representability properties of feed-forward neural networks." In International Neural Network Conference, 792. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_90.

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Lisa, F., J. Carrabina, C. Pérez-Vicente, N. Avellana, and E. Valderrama. "Feed forward network for vehicle license character recognition." In New Trends in Neural Computation, 638–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-56798-4_214.

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Kumar, P. N., G. Rahul Seshadri, A. Hariharan, V. P. Mohandas, and P. Balasubramanian. "Financial Market Prediction Using Feed Forward Neural Network." In Communications in Computer and Information Science, 77–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20209-4_11.

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Müller, Peter, and David Rios Insua. "Posterior Simulation for Feed Forward Neural Network Models." In COMPSTAT, 385–90. Heidelberg: Physica-Verlag HD, 1996. http://dx.doi.org/10.1007/978-3-642-46992-3_51.

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Skansi, Sandro. "Modifications and Extensions to a Feed-Forward Neural Network." In Undergraduate Topics in Computer Science, 107–20. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73004-2_5.

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Kotwal, Adit, Jai Kotia, Rishika Bharti, and Ramchandra Mangrulkar. "Training a Feed-Forward Neural Network Using Cuckoo Search." In Springer Tracts in Nature-Inspired Computing, 101–22. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5163-5_5.

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Milosevic, Stefan, Timea Bezdan, Miodrag Zivkovic, Nebojsa Bacanin, Ivana Strumberger, and Milan Tuba. "Feed-Forward Neural Network Training by Hybrid Bat Algorithm." In Modelling and Development of Intelligent Systems, 52–66. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68527-0_4.

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Conference papers on the topic "Feed Forward Neural Network (FFNN)"

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Dambrosio, Lorenzo, Marco Bomba, Sergio M. Camporeale, and Bernardo Fortunato. "Feed Forward Neural Network-Based Diagnostic Tool for Gas Turbine Power Plant." In ASME Turbo Expo 2002: Power for Land, Sea, and Air. ASMEDC, 2002. http://dx.doi.org/10.1115/gt2002-30019.

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A diagnostic tool able to detect faults that may occur in a gas turbine power plant at an early stage of their emergence is of a great importance for power production. In the present paper, a diagnostic tool, based on Feed Forward Neural Networks (FFNN), has been proposed for gas turbine power plants with a condition monitoring approach. The main aim of the proposed diagnostic tool is to reliably detect not only every considered single fault, but also two or more faults that may occur contemporarily. Two different FFNNs compose the proposed diagnostic tool. The first network, that is not-fully connected, operates a fault pre-processing in order to evaluate the influence of the single fault variable on the single fault condition. The second FFNN detects the fault conditions by means of an iterative process. Such a diagnostic tool has been applied to a mathematical model of a single shaft gas turbine for power generation, resulting able to detect the 100% of single faults and the 80% of combined faults.
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Weerasinghe, Y. S. P., M. W. P. Maduranga, and M. B. Dissanayake. "RSSI and Feed Forward Neural Network (FFNN) Based Indoor Localization in WSN." In 2019 National Information Technology Conference (NITC). IEEE, 2019. http://dx.doi.org/10.1109/nitc48475.2019.9114515.

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Camporeale, S., L. Dambrosio, A. Milella, M. Mastrovito, and B. Fortunato. "Fault Diagnosis of Combined Cycle Gas Turbine Components Using Feed Forward Neural Networks." In ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/gt2003-38742.

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A diagnostic tool based on Feed Forward Neural Networks (FFNN) is proposed to detect the origin of performance degradation in a Combined Cycle Gas Turbine (CCGT) power plant. In such a plant, due the connection of the steam cycle to the gas turbine, any deterioration of gas turbine components affects not only the gas turbine itself but also the steam cycle. At the same time, fouling of the heat recovery boiler may cause the increase of the turbine back-pressure, reducing the gas turbine performance. Therefore, measurements taken from the steam cycle can be included in the fault variable set, used for detecting faults in the gas turbine. The interconnection of the two parts of the CCGT power plant is shown through the fingerprints of selected component fault models for a power plant composed of a heavy-duty gas turbine and a steam plant with a single pressure recovery boiler. The diagnostic tool is composed of two FFNN stages: the first network stage is addressed to pre-process fault data in order to evaluate the influence of the single fault variable on the single fault condition. The second FFNN stage detects the fault conditions. Tests with simulated data show that the the diagnostic tool is able to recognize single faults of both the gas turbine and the steam plant, with a high rate of success, in case of full fault intensity, even in presence of uncertainties in measurements. In case of partial fault intensity, faults concerning gas turbine components and the superheater, are well recognized, while false alarms occur for the other steam plant component faults, in presence of uncertainties in data. Finally, some combinations of faults, belonging either to the gas turbine or the steam plant, have been examined for testing the diagnostic tool on double fault detection. In this case, the network is applied twice. In the first step the amount of the fault parameters that originate the primary fault are estimated. In the second step, the diagnostic tool curtails the contribution of the main fault to the fault parameters, and the diagnostic process is reiterated. In the examined fault combinations, the diagnostic tool was able to detect at least one of the two faults in about 60% of the cases, even in presence of uncertainty in measurements and partial fault intensity.
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Adege, Abebe Belay, Lei Yen, Hsin-piao Lin, Yirga Yayeh, Yun Ruei Li, Shiann-Shiun Jeng, and Getaneh Berie. "Applying Deep Neural Network (DNN) for large-scale indoor localization using feed-forward neural network (FFNN) algorithm." In 2018 IEEE International Conference on Applied System Innovation (ICASI). IEEE, 2018. http://dx.doi.org/10.1109/icasi.2018.8394387.

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Fullerton, Anne M., Thomas C. Fu, and David E. Hess. "Investigation and Prediction of Wave Impact Loads on Ship Appendage Shapes." In ASME 2007 26th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2007. http://dx.doi.org/10.1115/omae2007-29217.

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Navy fleet problems with damage to hatches and other appendages after operation in high sea states suggest that wave impact loads may be greater than the current design guidelines of 1000 pounds per square foot (48 kilopascal) (Ship Specification Section 100, General Requirements for Hull Structure and Guidance Manual for Temporary Alterations, NAVSEA S9070-AA-MME-010/SSN, SSBN). These large impact forces not only cause damage to ships and ship structures, they can also endanger the ship’s crew. To design robust marine structures, accurate estimates of all encountered loads are necessary, including the wave impact forces, which are complex and involve wave breaking, making them difficult to estimate numerically. An experiment to investigate wave impact loads was performed at the Naval Surface Warfare Center, Carderock Division in 2005. During this experiment, the horizontal and vertical loads of regular, non-breaking waves on a 12 inch (0.305 m) square plate and a 19.75 inch (0.5 m) diameter horizontal cylinder were measured while varying incident wave height, wavelength, wave steepness, plate angle and immersion level of the plate and cylinder. Wave heights of up to 1.5 feet (0.46 m) were tested, with wavelenghs of up to 30 feet (9.1 m). In all cases, the horizontal wave impact force increased with wave steepness. For some angles, the horizontal wave impact force increased with greater submergence. A feed-forward neural network (FFNN) developed by Applied Simulation Technologies was used to predict the horizontal forces measured during the experiment based on the values of wave height, wavelength, wave steepness, plate angle and immersion level of the plate and cyclinder. A FFNN is a computational method used to develop nonlinear equation systems that use input variables to predict output variables. Predictions of forces from the FFNN compare well with the experimental data, and may be useful in future design of ships and ship structures.
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Devi, Bharathi B. "Probabilistic feed-forward neural network." In Photonics for Industrial Applications, edited by David P. Casasent. SPIE, 1994. http://dx.doi.org/10.1117/12.188906.

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Rosay, Arnaud, Florent Carlier, and Pascal Leroux. "Feed-forward neural network for Network Intrusion Detection." In 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring). IEEE, 2020. http://dx.doi.org/10.1109/vtc2020-spring48590.2020.9129472.

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Zhao, Huiqing. "Neural Network Blind Equalization Algorithm Based on Feed Forward Neural Network." In First International Conference on Information Science and Electronic Technology (ISET 2015). Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/iset-15.2015.31.

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Tamura. "On interpretations of a feed-forward neural network." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118350.

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Zhou, Wengang, Leiting Dong, Lubomir Bic, Mingtian Zhou, and Leiting Chen. "Internet traffic classification using feed-forward neural network." In 2011 International Conference on Computational Problem-Solving (ICCP). IEEE, 2011. http://dx.doi.org/10.1109/iccps.2011.6092257.

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Reports on the topic "Feed Forward Neural Network (FFNN)"

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Arhin, Stephen, Babin Manandhar, Hamdiat Baba Adam, and Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, April 2021. http://dx.doi.org/10.31979/mti.2021.1943.

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Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) data for six Washington Metropolitan Area Transit Authority (WMATA) bus routes operating in Washington, DC. We developed regression models and Artificial Neural Network (ANN) models for predicting travel times of buses for different peak periods (AM, Mid-Day and PM). Our analysis included variables such as number of served bus stops, length of route between bus stops, average number of passengers in the bus, average dwell time of buses, and number of intersections between bus stops. We obtained ANN models for travel times by using approximation technique incorporating two separate algorithms: Quasi-Newton and Levenberg-Marquardt. The training strategy for neural network models involved feed forward and errorback processes that minimized the generated errors. We also evaluated the models with a Comparison of the Normalized Squared Errors (NSE). From the results, we observed that the travel times of buses and the dwell times at bus stops generally increased over time of the day. We gathered travel time equations for buses for the AM, Mid-Day and PM Peaks. The lowest NSE for the AM, Mid-Day and PM Peak periods corresponded to training processes using Quasi-Newton algorithm, which had 3, 2 and 5 perceptron layers, respectively. These prediction models could be adapted by transit agencies to provide the patrons with accurate travel time information at bus stops or online.
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