Dissertations / Theses on the topic 'Cascade neural networks'
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Obiegbu, Chigozie. "Image compression using cascaded neural networks." ScholarWorks@UNO, 2003. http://louisdl.louislibraries.org/u?/NOD,51.
Full textTitle from electronic submission form. "A thesis ... in partial fulfillment of the requirements for the degree of Master of Science in the Department of Electrical Engineering"--Thesis t.p. Vita. Includes bibliographical references.
Rivest, François. "Knowledge transfer in neural networks : knowledge-based cascade-correlation." Thesis, McGill University, 2002. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=29470.
Full textBoudani, Nabil I. "Cascade artificial neural networks technique for solving ellipsometry problems." FIU Digital Commons, 1998. http://digitalcommons.fiu.edu/etd/1781.
Full textSenalp, Erdem Turker. "Cascade Modeling Of Nonlinear Systems." Phd thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/3/12608578/index.pdf.
Full text2) Introduction of B-Spline curve nonlinearity representations instead of polynomials in cascade modeling. As a result, local control in nonlinear system modeling is achieved. Thus, unexpected variations of the output can be modeled more closely. As an important demonstration case, a model is developed and named as Middle East Technical University Neural Networks and Cascade Model (METU-NN-C). Application examples are chosen by considering the Near-Earth space processes, which are important for navigation, telecommunication and many other technical applications. It is demonstrated that the models developed based on the contributions of this work are especially more accurate under disturbed conditions, which are quantified by considering Space Weather parameters. Examples include forecasting of Total Electron Content (TEC), and mapping
estimation of joint angle of simple forced pendulum
estimation of joint angles of spring loaded inverted double pendulum with forced table
identification of Van der Pol oscillator
and identification of speakers. The operation performance results of the International Reference Ionosphere (IRI-2001), METU Neural Networks (METU-NN) and METU-NN-C models are compared qualitatively and quantitatively. As a numerical example, in forecasting the TEC by using the METU-NN-C having Bezier curves in nonlinearity representation, the average absolute error is 1.11 TECu. The new cascade models are shown to be promising for system designers and operators.
Černík, Tomáš. "Neuronové sítě s proměnnou topologií." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255440.
Full textKannan, Suresh Kumar. "Adaptive Control of Systems in Cascade with Saturation." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7566.
Full textRiley, Mike J. W. "Evaluating cascade correlation neural networks for surrogate modelling needs and enhancing the Nimrod/O toolkit for multi-objective optimisation." Thesis, Cranfield University, 2011. http://dspace.lib.cranfield.ac.uk/handle/1826/6796.
Full textGervini, Vitor Irigon. "Modelagem e controle de um servoposicionador pneumático via redes neurais." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2014. http://hdl.handle.net/10183/110080.
Full textThe development of a precise positioning system has motivated several researches in the pneumatic systems control area to overcome the problems caused by these nonlinearities, by appropriate feedback control algorithms. In this work it is proposed a methodology based on neural networks to achieve accurate mathematical models that can be used in simulation as in controllers techniques based on models. This methodology was tested through its application in identifying the phenomenon of friction and the relationship pressure/mass flow through servo valve orifices control holes. Furthermore, using neural networks, the inverse relationship between the desired flow rates and control signal of servo valve (diffeomorphism), which is applied in various control techniques based on models, was determined. To evaluate the proposed modeling methodology, simulations were done in open and closed loop, and the results were compared with experiments conducted on a real pneumatic servo positioning system. A neural network based model was used to develop a nonlinear controller according to a cascade strategy with friction compensation (which has been tested on other studies showing satisfactory results when applied to pneumatic servo positioning control). The cascade control strategy, despite showing a good performance in trajectory tracking, presents significant difficulties in implementation due mainly to difficulties associated with the system parameters identification process, which are especially expensive. The characteristics of the closed loop stability were analyzed by Lyapunov method. The experimental results obtained in closed loop attest the efficiency of the proposed control strategy.
Borges, Fábio Augusto Pires. "Controle em cascata de um atuador hidráulico utilizando redes neurais." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/165587.
Full textIn this work, the modeling and identification of a hydraulic actuator testing setup are performed and the analytical expressions that are used in a cascade control strategy applyied in a position trajectory tracking control are designed. Such cascade strategy uses the feedback linearization control law in the hydraulical subsystem and the Slotine and Li control law in the mechanical one. Based on this cascade strategy, a neural cascade controller is proposed, for which the analytical function used as inversion set in the feedback linearization control law and the friction function compensation of the Slotine and Li control law are replaced by multi layer perceptrons neural networks where the inputs are the states of the system and the hydraulic fluid temperature. The novel controller is introduced in two different aproachs: the first one where the neural networks do not have on-line modifications and the second one where adaptive control laws are proposed. For both of them the stability proof of the closed-loop system is presented. Experimental results about some position tracking controls performed in different fluid temperature are showed. The results show that the novel controller is more efective than the classical PID, PID+feedforward and the traditional analytical cascade controller. The experiments are performed in two different setups: considering the system without importants parametric variations where is applied the non adaptive cascade neural controller and in the presence of parametric variations where is applied the adaptive cascade neural controller.
Eklund, Anton. "Cascade Mask R-CNN and Keypoint Detection used in Floorplan Parsing." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-415371.
Full textReeder, John. "Life Long Learning in Sparse Learning Environments." Doctoral diss., University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5845.
Full textPh.D.
Doctorate
Electrical Engineering and Computing
Engineering and Computer Science
Computer Engineering
Stuner, Bruno. "Cohorte de réseaux de neurones récurrents pour la reconnaissance de l'écriture." Thesis, Normandie, 2018. http://www.theses.fr/2018NORMR024.
Full textState-of-the-art methods for handwriting recognition are based on LSTM recurrent neural networks (RNN) which achieve high performance recognition. In this thesis, we propose the lexicon verification and the cohort generation as two new building blocs to tackle the problem of handwriting recognition which are : i) the large vocabulary problem and the use of lexicon driven methods ii) the combination of multiple optical models iii) the need for large labeled dataset for training RNN. The lexicon verification is an alternative to the lexicon driven decoding process and can deal with lexicons of 3 millions words. The cohort generation is a method to get easily and quickly a large number of complementary recurrent neural networks extracted from a single training. From these two new techniques we build and propose a new cascade scheme for isolated word recognition, a new line level combination LV-ROVER and a new self-training strategy to train LSTM RNN for isolated handwritten words recognition. The proposed cascade combines thousands of LSTM RNN with lexicon verification and achieves state-of-the art word recognition performance on the Rimes and IAM datasets. The Lexicon Verified ROVER : LV-ROVER, has a reduce complexity compare to the original ROVER algorithm and combine hundreds of recognizers without language models while achieving state of the art for handwritten line text on the RIMES dataset. Our self-training strategy use both labeled and unlabeled data with the unlabeled data being self-labeled by its own lexicon verified predictions. The strategy enables self-training with a single BLSTM and show excellent results on the Rimes and Iam datasets
Малишевська, Катерина Миколаївна. "Інтелектуальна система для розпізнавання об'єктів на оптичних зображеннях з використанням каскадних нейронних мереж." Doctoral thesis, Київ, 2015. https://ela.kpi.ua/handle/123456789/14391.
Full textChau, Edward Yu-Ho. "Adaptive noise reduction using a cascaded hybrid neural network." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ61882.pdf.
Full textReed, Stuart. "Cascaded linear shift invariant processing in pattern recognition." Thesis, Loughborough University, 2000. https://dspace.lboro.ac.uk/2134/7481.
Full textHussain, Saed. "Fault tolerant flight control : an application of the fully connected cascade neural network." Thesis, University of Central Lancashire, 2015. http://clok.uclan.ac.uk/12123/.
Full textJacobsson, Henrik. "A Comparison of Simple Recurrent and Sequential Cascaded Networks for Formal Language Recognition." Thesis, University of Skövde, Department of Computer Science, 1999. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-391.
Full textTwo classes of recurrent neural network models are compared in this report, simple recurrent networks (SRNs) and sequential cascaded networks (SCNs) which are first- and second-order networks respectively. The comparison is aimed at describing and analysing the behaviour of the networks such that the differences between them become clear. A theoretical analysis, using techniques from dynamic systems theory (DST), shows that the second-order network has more possibilities in terms of dynamical behaviours than the first-order network. It also revealed that the second order network could interpret its context with an input-dependent function in the output nodes. The experiments were based on training with backpropagation (BP) and an evolutionary algorithm (EA) on the AnBn-grammar which requires the ability to count. This analysis revealed some differences between the two training-regimes tested and also between the performance of the two types of networks. The EA was found to be far more reliable than BP in this domain. Another important finding from the experiments was that although the SCN had more possibilities than the SRN in how it could solve the problem, these were not exploited in the domain tested in this project
Kolman, Aleš. "Detekce obličejů ve videu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236583.
Full textLång, Ivar. "Utvärdering av Artificiella Neurala Arkitekturer För Navigering." Thesis, Högskolan i Skövde, Institutionen för kommunikation och information, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-4952.
Full textНафас, Агаї Аг Гаміш Ові. "Прогнозування ризику банкрутства в промисловій та банківській сфері з використанням нечітких моделей та алгоритмів." Thesis, НТУУ "КПІ", 2016. https://ela.kpi.ua/handle/123456789/14938.
Full textThe thesis is devoted to the development of models and algorithms for analysis of financial state and forecasting of bankruptcy risk of enterprises and banks in condition of uncertainty, incomplete and unreliable information on the example of the Ukrainian economy. Classical statistical methods for predicting the risk of bankruptcy on the basis of multivariate discriminant analysis, in particular the method of Altman, are analyzed. It revealed its deficiencies and inappropriateness of its use in Ukraine's economy, since it is based on the use of reliable information on the state enterprises. Therefore, the use of fuzzy neural networks (FNN) with the conclusions Mamdani and Tsukamoto to forecast the risk of bankruptcy in the conditions of incompleteness and uncertainty is entirely justified. In the thesis rule base is developed for solving the problem of financial analysis and forecasting the risk of bankruptcy of enterprises for neural networks Mamdani and Tsukamoto. Since the total size of the comprehensive fuzzy rule base is great that does not allow its training in a short time, a method of reducing the size of the rule base and its visual representation through the use of scores is suggested. Algorithms for predicting the risk of bankruptcy of enterprises with FNN Mamdani and Tsukamoto are developed. Further in the paper the cascade neo-fuzzy network (CNFN) for predicting the risk of bankruptcy in condition of uncertainty is suggested. Its features is the absence of the rule base, as well as the fact that the membership functions are fixed and does not need training. Therefore, these networks have accelerated the convergence of training compared with FNN Mamdani and Tsukamoto. Experimental studies of the proposed models and algorithms for the forecasting of the risk of bankruptcy in Ukraine and comparative analysis with classical methods are presented. The experimental results showed that the accuracy of predicting the bankruptcy risk by Altmana- by 68- 70%, matrix method - 80%, cascade neo-fuzzy neural network - 87% and FNN Mamdanі and Tsukamoto - 88-90%. The paper also studied the problem of forecasting the risk of bankruptcy in the banking sector of Ukraine in conditions of uncertainty. To solve this problem using FNN TSK and ANFIS is proposed. Experimental research of effectiveness of using FNN to predict the risk of bank failures and comparison with statistical models ARIMA, logit-model, probit-model and fuzzy GMDH are presented. The experiment established that the greatest prediction accuracy allows the use of FNN TSK (2%) and fuzzy GMDH (4%), while the statistical models: logit-model - 16%, probit-model - 14% and ARIMA - 18%. During the experiments adequate financial and economic indicators of banks to predict the risk of bankruptcy were determined.
Диссертация посвящена разработке моделей и алгоритмов анализа финансового состояния и прогнозирования риска банкротства предприятий и банков в условиях неопределенности, неполной и недостоверной информации на примере экономики Украины. Проанализированы классические статистические методы прогнозирования риска банкротства предприятий на основе методов многомерного дискриминантного анализа, в частности метод Альтмана. Выявлено его недостатки и нецелесообразность использования в условиях экономики Украины, поскольку он базируется на использовании достоверной информации о состоянии предприятий. Поэтому в работе обосновано использование для прогнозирования риска банкротства в условиях неполноты и неопределенности нечетких нейронных сетей (ННС) с выводами Мамдани и Цукамото. В дисертации разработана база правил для решения задачи анализа финансового состояния и прогнозирования риска банкротства предприятий в условиях неопределенности для нейросетей Мамдани и Цукамото. Поскольку общий размер полной базы нечетких правил большой, что не дает возможности ее обучения за короткое время, предложен способ сокращения размеров базы правил и ее наглядное представление путем использования балльных оценок. Разработаны алгоритмы прогнозирования риска банкротства предприятий с использованием ННС Мамдани и Цукамото. Далее в работе рассмотрены каскадные нео-фаззи сети для прогнозирования риска банкротства предприятий в условиях неопределенности. Их особенностями является отсутствие базы правил вывода, а также то, что функции принадлежностей фиксированные и не нуждаются в обучении, обучаются лишь линейные параметры – веса связей ННС. Поэтому эти сети имеют ускоренную сходимость обучения в сравнении с ННС Мамдани и Цукамото. Проведены экспериментальные исследования предложенных моделей и алгоритмов для прогнозирования риска банкротства предприятий Украины и сравнительный анализ с классическими методами. Результаты экспериментов показали, что точность прогнозирования риска банкротства составляет методом Альтмана - 68-70%, матричным методом - 80%, нео-фаззи каскадной нейросетью - 87%, а ННМ Мамдани и Цукамото -88-90 %. В работе также была исследована проблема прогнозирования риска банкротства в банковской сфере Украины в условиях неопределенности. Для решения этой проблемы предложено использование ННС TSK и ANFIS. Проведены экспериментальные исследования эффективности использования ННС для прогнозирования риска банкротства банков и сравнение со статистическими моделями ARIMA, logit-model и probit–model, а также с нечетким МГУА. В результате экспериментов установлено, что самую большую точность прогнозирования обеспечивает использование ННМ TSK (2%) и нечеткий МГУА (4%), тогда как статистические модели имеют точность: logit-model - 16%, probit–model - 14% и ARIMA - 18%. В процессе экспериментов были также определены адекватные финансово-экономические показатели банков для прогнозирования риска банкротства.
Cardozo, López Sergio Daniel. "Otimização de placas e cascas de materiais compósitos, utilizando algoritmos genéticos, redes neurais e elementos finitos." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2009. http://hdl.handle.net/10183/18583.
Full textStructural optimization using computational tools has become a major research field in recent years. Methods commonly used in structural analysis and optimization may demand considerable computational cost, depending on the problem complexity. Therefore, many techniques have been evaluated in order to diminish such impact. Among these various techniques, artificial neural networks may be considered as one of the main alternatives, when combined with classic analysis and optimization methods, to reduce the computational effort without affecting the final solution quality. Use of laminated composite structures has been continuously growing in the last decades due to the excellent mechanical properties and low weight characterizing these materials. Taken into account the increasing scientific effort in the different topics of this area, the aim of the present work is the formulation and implementation of a computational code to optimize manufactured complex laminated structures with a relatively low computational cost by combining the Finite Element Method (FEM) for structural analysis, Genetic Algorithms (GA) for structural optimization and Artificial Neural Networks (ANN) to approximate the finite element solutions. The modules for linear and geometrically non-linear static finite element analysis and for optimize laminated composite plates and shells, using GA, were previously implemented. Here, the finite element module is extended to analyze dynamic responses to optimize problems based in frequencies and modal criteria, and a module with perceptron ANN is added to approximate finite element analyses. Several examples are presented to show the effectiveness of ANN to approximate solutions obtained using the FEM and to reduce significatively the computational cost.
Paduru, Anirudh. "Fast Algorithm for Modeling of Rain Events in Weather Radar Imagery." ScholarWorks@UNO, 2009. http://scholarworks.uno.edu/td/1097.
Full textVopálenský, Radek. "Detekce, sledování a klasifikace automobilů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2017. http://www.nusl.cz/ntk/nusl-413327.
Full textVopálenský, Radek. "Detekce, sledování a klasifikace automobilů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-385899.
Full textPřecechtěl, Roman. "Optimalizace řízení aktivního síťového prvku." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218166.
Full textSusnjak, Teo. "Accelerating classifier training using AdaBoost within cascades of boosted ensembles : a thesis presented in partial fulfillment of the requirements for the degree of Master of Science in Computer Sciences at Massey University, Auckland, New Zealand." Massey University, 2009. http://hdl.handle.net/10179/1002.
Full textOsório, Fernando Santos. "Inss : un système hybride neuro-symbolique pour l'apprentissage automatique constructif." Grenoble INPG, 1998. https://tel.archives-ouvertes.fr/tel-00004899.
Full textVarious Artificial Intelligence methods have been developed to reproduce intelligent human behaviour. These methods allow to reproduce some human reasoning process using the available knowledge. Each method has its advantages, but also some drawbacks. Hybrid systems combine different approaches in order to take advantage of their respective strengths. These hybrid intelligent systems also present the ability to acquire new knowledge from different sources and so to improve their application performance. This thesis presents our research in the field of hybrid neuro-symbolic systems, and in particular the study of machine learning tools used for constructive knowledge acquisition. We are interested in the automatic acquisition of theoretical knowledge (rules) and empirical knowledge (examples). We present a new hybrid system we implemented: INSS - Incremental Neuro-Symbolic System. This system allows knowledge transfer from the symbolic module to the connectionist module (Artificial Neural Network - ANN), through symbolic rule compilation into an ANN. We can refine the initial ANN knowledge through neural learning using a set of examples. The incremental ANN learning method used, the Cascade-Correlation algorithm, allows us to change or to add new knowledge to the network. Then, the system can also extract modified (or new) symbolic rules from the ANN and validate them. INSS is a hybrid machine learning system that implements a constructive knowledge acquisition method. We conclude by showing the results we obtained with this system in different application domains: ANN artificial problems(The Monk's Problems), computer aided medical diagnosis (Toxic Comas), a cognitive modelling task (The Balance Scale Problem) and autonomous robot control. The results we obtained show the improved performance of INSS and its advantages over others hybrid neuro-symbolic systems
Špaňhel, Jakub. "Re-identifikace vozidla pomocí rozpoznání jeho registrační značky." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-264932.
Full textLudwig, Oswaldo. "Study on non-parametric methods for fast pattern recognition with emphasis on neural networks and cascade classifiers." Doctoral thesis, 2012. http://hdl.handle.net/10316/19900.
Full textEsta tese concentra-se em reconhecimento de padrões, com particular ênfase para o con ito de escolha entre capacidade de generalização e custo computacional, a m de fornecer suporte para aplicações em tempo real. Neste contexto são apresentadas contribuições metodológicas e analíticas para a abordagem de dois tipos de datasets: balanceados e desbalanceados. Um dataset é denominado balanceado quando há um número aproximadamente igual de observações entre as classes, enquanto datasets que têm números desiguais de observações entre as classes são denominados desbalanceados, tal como ocorre no caso de detecção de objetos baseada em imagem. Para datasets balanceados é adoptado o perceptrão multicamada (MLP) como classi cador, uma vez que tal modelo é um aproximador universal, ou seja MLPs podem aproximar qualquer conjunto de dados. Portanto, ao invés de propor novos modelos de classi cadores, esta tese concentra-se no desenvolvimento e análise de novos métodos de treinamento para MLP, de forma a melhorar a sua capacidade de generalização através do estudo de quatro abordagens diferentes: maximização da margem de classi cação, redundância, regularização, e transdução. A idéia é explorar novos métodos de treino para MLP com vista a obter classi cadores não-lineares mais rápidos que o usual SVM com kernel não-linear, mas com capacidade de generalização similar. Devido à sua função de decisão, o SVM com kernel não-linear exige um esforço computacional elevado quando o número de vetores de suporte é grande. No contexto dos datasets desbalanceados, adotou-se classi cadores em cascata, já que tal modelo pode ser visto como uma árvore de decisão degenerativa que realiza rejeições em cascata, mantendo o tempo de processamento adequado para aplicações em tempo real. Tendo em conta que conjuntos de classi cadores são susceptíveis a ter alta dimensão VC, que pode levar ao over- tting dos dados de treino, foram deduzidos limites para a capacidade de generalização dos classi cadores em cascata, a m de suportar a aplicação do princípio da minimização do risco estrutural (SRM). Esta tese também apresenta contribuições na seleção de características e dados de treinamento, devido à forte in uência que o pre-processamento dos dados tem sobre o reconhecimento de padrões. Os métodos propostos nesta tese foram validados em vários datasets do banco de dados da UCI. Alguns resultados experimentais já podem ser consultados em três revistas da ISI, outros foram submetidos a duas revistas e ainda estão em processo de revisão. No entanto, o estudo de caso desta tese é limitado à detecção e classi cação de peões.
This thesis focuses on pattern recognition, with particular emphasis on the trade o between generalization capability and computational cost, in order to provide support for on-the- y applications. Within this context, two types of datasets are analyzed: balanced and unbalanced. A dataset is categorized as balanced when there are approximately equal numbers of observations in the classes, while unbalanced datasets have unequal numbers of observations in the classes, such as occurs in case of imagebased object detection. For balanced datasets it is adopted the multilayer perceptron (MLP) as classi er, since such model is a universal approximator, i.e. MLPs can t any dataset. Therefore, rather than proposing new classi er models, this thesis focuses on developing and analysing new training methods for MLP, in order to improve its generalization capability by exploiting four di erent approaches: maximization of the classi cation margin, redundancy, regularization, and transduction. The idea is to exploit new training methods for MLP aiming at an nonlinear classi er faster than the usual SVM with nonlinear kernel, but with similar generalization capability. Note that, due to its decision function, the SVM with nonlinear kernel requires a high computational e ort when the number of support vectors is big. For unbalanced datasets it is adopted the cascade classi er scheme, since such model can be seen as a degenerate decision tree that performs sequential rejection, keeping the processing time suitable for on-the- y applications. Taking into account that classi er ensembles are likely to have high VC dimension, which may lead to over- tting the training data, it were derived generalization bounds for cascade classi ers, in order to support the application of structural risk minimization (SRM) principle. This thesis also presents contributions on feature and data selection, due to the strong in uence that data pre-processing has on pattern recognition. The methods proposed in this thesis were validated through experiments on several UCI benchmark datasets. Some experimental results can be found in three ISI journals, others has been already submitted to two ISI journals, and are under review. However, the case study of this thesis is limited to pedestrian detection and classi cation.
Lu, Ko-Ping, and 盧可平. "Applying Cascade Neural Network to analyze Energy Saving of Chiller." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/kq6cuz.
Full text國立臺北科技大學
能源與冷凍空調工程系
106
Three methods are applied in this study: linear regression, backpropagation network and cascade forward backpropagation network. The power consumption models, before cleaning the condenser, are established by using these three methods. We collect data of the chillers after cleaning condenser, then simulate the power consumption of the chillers before cleaning by using these three models. After that, the simulate results and improvement of performance are analyzed and compared by using three methods under the same baseline. In this study, the model established by cascade forward backpropagation network is more accurate and have less error than the models established by linear regression and backpropagation network. In the result, we realized that the effects of energy saving after we cleaned the condenser in case 1 is about 5.1% and in case 2 is 3.77%. In case 3, the results show that replacing hydrocarbon refrigerant R-290 can get about 25% of energy saving. Generally, using neural network to do simulation will get high accuracy. Cascade forward backpropagation network can consider the original input so that can make the results more accurate.
Kohl, Nate F. "Learning in fractured problems with constructive neural network algorithms." 2009. http://hdl.handle.net/2152/10658.
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Hsiao, Ning-Chun, and 蕭寧諄. "Face detection and recognition based on a cascaded convolutional neural network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/4nyv28.
Full text國立中央大學
資訊工程學系
106
In recent years, thanks to the development of CNN (convolutional neural network), researchers have made great progress on face detection and face recognition. Many unique and novel network structures have been proposed to solve different face detection or recognition problems. To use which network structure depends on the application, for example, we only need to perform face recognition on an image with only one face at customs. However, in monitoring or access control system, we need to perform face detection first to find where faces are and then recognize every faces. We propose a CNN structure which combines face detection and face recognition. We use the RPN structure from Faster R-CNN to propose candidate regions which may be faces. We then use a coarse-to-fine cascaded CNN to check each candidate regions and filter out the regions which are not faces. By using RPN structure instead of using sliding widow to propose candidate region, we can avoid checking regions in every sizes and at every places one by one. The system needs only 0.08 seconds with RPN structure, compared to 0.18 seconds with sliding window method, we get better execution speed, and the detection capability remains nearly the same. After finishing face detection, we then use FaceNet to extract features for recognition. Due to the definition of the loss function, the distance between two feature vectors extracted from two facial images can reflect the similarity of the two facial images. That is, we can recognize faces by only calculate the distance between feature vectors without using any complex classifiers, which allows us to use the same recognition system in different situations. The recognition accuracy of the proposed method can reach 97%, which is slightly lower than the methods that need to be retrained. However, considering the convenience of using the same recognition system without retraining, we think it’s still a great deal.
Chen, Yen-Feng, and 陳彥灃. "Facial Gender Classification based on Convolutional Neural Networks Cascaded with Discrete Wavelet Transform." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/05937291010012756825.
Full text國立暨南國際大學
電機工程學系
105
Facial gender classification based on deep convolutional neural networks (DCNNs) cascaded with discrete wavelet transform (DWT) is a work in biometrics by means of the deep learning (DL) architecture. The applied DL architecture is convolutional neural networks (CNNs) which is appropriate to image processing. Biometrics is a subject of pattern recognition while deep learning is a novel machine learning architecture, which is considered the most powerful classifier among the state-of-the-art. The architecture of this work is that three specified channels of CNNs connected in parallel are cascaded with three frequency bands of DWT, respectively. The DWT process extracts critical features from input faces by which the number of kernel of the CNNs can be reduced. This will reduce the training time and the impact of random. Adience 3D dataset is applied as the benchmark.
LIN, JIN-ZHOU, and 林晉州. "Applying Cascade Neural Network and Simulated Annealing to Optimal Loading for Hybrid Chiller Systems." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/qsk546.
Full text國立臺北科技大學
能源與冷凍空調工程系
107
Manual method currently used is not efficient, resulting in wasted large amounts of energy. If we can use optimal chiller loading method to meet the system requirements , the total power consumption of the chillers will be minimized. This study uses the cascade forward backpropagation network to establish chiller power consumption models, which considers the operating constrains of each chillers. Simulated annealing is also integrated while satisfying cooling load conditions to optimize chiller loading.The simulation result show that integrating cascade forward backpropagation network and simulated annealing between 95% and 55% cooling loads improved power saving compared to manual method load distribution, saving maximum total power consumption by approximately 19% in75% load. Compared with the application of cascade forward backpropagation network integrated with genetic algorithm, the maximum error is about 3.4%, The results of the two algorithms are not much different, but the calculation time difference is about more than 1 minute.
Huang, BeenLi, and 黃本立. "A Study of the Cascade-Correlation Learning Neural Network Applied in Chemical-Mechanical Polishing Process." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/75902008002664995540.
Full text國立臺灣科技大學
機械工程系
87
Chemical-mechanical planarization is a technique that used to polish the surface of a wafer and make it planner. In the planarization process, the major factors such as material remove rate, non-unfority and wafer surface reside stress will be changed with the process parameters set up. It will inference the yield rate of CMP process. In this reserch both of the 3D ocmputer simulation model of CMP, as well as a Neural Network was used to simulate and predict the resoult of CMP, so that a 3D polish mechanism model of CMP was constructed by CAD. This model was used to analyze and simulate the polish conditions. In the same time based on the theory of Neural Network which is the cascade correction learning model, with the same parameters used in the 3D mechanism model such as revolution rate of the carrier, back pressure of carieer, revolution rate of plan and characteristics of slurry etc. as the input parameters of the Neural Network, to training this Network, and form a smart Neural Network. According to both CAD model and Neural Network model, can simulate the CMP process, and optimize the input parameters for the real CMP operation.
王柏文. "Applying Cascade-Correlation Neural Network to Recognizing Patterns in the Time Series of TAIEX Futures." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/08430933105142093503.
Full text國立交通大學
資訊管理研究所
94
Previous researchs claimed that to forecast the index movement by using the neural network, massive amount of waiting time was normally mandatory. The situation gets worse if larger training sets and more hidden units come into the iteration. Thisphenomenon explains why Backprop neural network takes time in learning process. In this paper, we propose a modified version of Backprop neural network, a cascade-correlation neural network, to simplify the configuration of experiment and shorten the running time for deriving the forcast result. Experiment shows that under the same working environment, cascade-correlation neural network outperforms Backprop neural network. Profits for cascade correlationneural network, especially, exhibit a steady increase through the tesing period, while profits for Backprop neural network flucturates, despite the fact that they both earn positive profits.
Chen, Tsay-Juin, and 陳在鈞. "Neural Network Cascade Steepest Descendant Learning Algorithm with application on Precise Temperature Control Control of Injetion Molding Barrel System." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/97187407721790547040.
Full text國立中興大學
機械工程學系
86
"類神經網路"以其良好之非線性映射能力,己經被廣泛的應用於系統模式 建立、模糊隸屬函數調整、文字辨識及複雜之非線性系統控制 ?等方面 。本論文主要目的乃是希望將類神經網路應用於極非線性之射出成型機料 管系統鑑別與精密溫度控制上。為了對料管系統做更完整之鑑別,本研究 嘗試在類神經網路EBP(誤差逆傳遞學習法則)架構之下,由一階逼進著手 ,在簡單的原則下,以最陡負梯度法則及參數上下限設定的方式,針對每 一次權值更新,找出增進模式或批次模式相對應之最佳學習參數 及學習 慣量 參數值,使得網路能在最少的次數下收斂,且能有不錯的重現性, 電腦模擬證明本研究所提出之新法則在收斂速度與學習成功率上,皆較其 他方法優異。在本研究之第二部分,我們先將所發展之新類神經網路學習 法則應用於射出成型機料管系統之模擬上,實際實驗結果證明,本研究所 提出之新方法確實可對複雜之料管系統做有效的鑑別。完成之類神經網路 料管模式則進一步用來做為類神經網路式參數自調PID控制器之依據,實 際實驗結果亦證明此參數自調PID控制器能將料管溫度控制於 +0.5~-0.5 度內 Artificial neural networks, with its high learning and nonlinear mapping ability, have been successfully applied to many system identification and control problems. The goal of this thesis is to apply the neural network techniques to the system identification and precise temperature control of the extremely nonlinear injection molding barrel system.In order to complete the system identification work as accuracy as possible, we propose a new and efficient multilayer neural network learning algorithm first. In this new learning algorithm (Cascade steepest descendant learning algorithm) the steepest descendant method is used to search the optimal learning constant ηand momentum term αfor each weights updating process. The well known Delta learning rule is then employed to modify the connecting weights in terms of the optimal ηand α. Computer simulations show that the proposed new algorithm outmatches other learning algorithms both in converging speed and success rate.In the second part of this research, we first apply the new neural network learning algorithm to the identification of the injection molding barrel system. Real experiment results demonstrate that the new algorithm can precisely identify the complicate barrel system. Further more, a self-tuning PID controller based on the trained neural network barrel model for precise temperature control is developed. Real experiments show that the proposed self-tuning PID controller can precisely control the barrel temperature within 0.5 degree.
Yen, Yu-shu, and 嚴玉書. "Cascaded Applications of the Artifical Neural Network Model of Typhoon Rainfall and the Semi-Distributed Runoff Model of Flood Simulation in a flood Simulation in a Flood Forecasting System." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/16775171200681302441.
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