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52nd International Universities Power Engineering Conference (UPEC)

作者： Kene, Raymond O. Olwal, Thomas O. Chowdhury, S. P. Daniel Tshwane Univ Technol Dept Elect Engn Pretoria South Africa

ISBN: (纸本)9781538623442

The most important investment decision when it comes to implementing photovoltaic (PV) array(s) either for commercial or private application, is the levelized cost of energy (LCOE) calculation. Whereas LCOE, used as an assessment tool, to calculate the cost effectiveness of energy generation in relation to the return over investment in a prescribed time, can be calculated with a simple mathematical equation;it remains unattainable without a proper systems dynamic modelling and performance prediction of PV modules in the array(s). PV cells are unpredictable and have very low conversion efficiency of about 15-25% which makes it often necessary that maximum power point tracking (MPPT) system is integrated to PV modules to get optimum energy yield. This draw back, makes it quite crucial that output power and performance prediction of PV arrays at module level are established as a precursor to PV solar system design and implementation. This is key to sustaining efficient energy production by the PV generators, as it helps to detect if the DC power output of the PV modules is at optimum. In reality;it is quite challenging to accurately predict the performance of PV array power output due to variable factors like solar insolation, sun incident angle, temperature, PV array configuration, dust and non-uniform illumination amongst many factors. All these factors create nonlinear output characteristics in PV modules which results into instability in energy yield, faults, losses and costly maintenance of the PV infrastructure. This research study presents a method for the performance prediction of PV modules in an array. It involves using neural network model with three layers (input, hidden and output) to predict PV module energy yield under uniform and non-uniform illumination (shaded conditions). Typical data like open circuit voltage (Voc), short circuit current (Isc), solar insolation and cell temperature are taken into consideration. Training datasets are obtained from th

关键词： PV energy LCOE performance prediction low efficiency neural network backpropagation algorithm MPPT

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Stability Analysis of Neural Networks-Based System Identification

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MODELLING AND SIMULATION IN ENGINEERING 2008年第unknown期2008卷

作者： Korkobi, Talel Djemel, Mohamed Chtourou, Andmohamed Univ Sfax Natl Engn Sch Sfax ENIS Res Unit Intelligent Control Design & Optimizat Complex Syst ICOS BP W Sfax 3038 Tunisia

This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm. Copyright (C) 2008 Talel Korkobi et al.

关键词： .Lyapunov neural network learning process System identification ectiveness learning rate convergence stability backpropagation algorithm CSBP

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Water quality assessment using diatom assemblages and advanced modelling techniques

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FRESHWATER BIOLOGY 2004年第2期49卷 208-220页

作者： Gevrey, M Rimet, F Park, YS Giraudel, JL Ector, L Lek, S Univ Toulouse 3 LADYBIO CNRS UMR F-31062 Toulouse France CREBS Ctr Rech Publ Gabriel Lippmann Luxembourg Luxembourg

1. Two types of artificial neural networks procedures were used to define and predict diatom assemblage structures in Luxembourg streams using environmental data. 2. Self-organising maps (SOM) were used to classify samples according to their diatom composition, and multilayer perceptron with a backpropagation learning algorithm (BPN) was used to predict these assemblages using environmental characteristics of each sample as input and spatial coordinates (X and Y) of the cell centres of the SOM map identified as diatom assemblages as output. Classical methods (correspondence analysis and clustering analysis) were then used to identify the relations between diatom assemblages and the SOM cell number. A canonical correspondence analysis was also used to define the relationship between these assemblages and the environmental conditions. 3. The diatom-SOM training set resulted in 12 representative assemblages (12 clusters) having different species compositions. Comparison of observed and estimated sample positions on the SOM map were used to evaluate the performance of the BPN (correlation coefficients were 0.93 for X and 0.94 for Y). Mean square errors of 12 cells varied from 0.47 to 1.77 and the proportion of well predicted samples ranged from 37.5 to 92.9%. This study showed the high predictability of diatom assemblages using physical and chemical parameters for a small number of river types within a restricted geographical area.

关键词： backpropagation algorithm benthic diatoms Kohonen self-organising map stream ecology

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LEARNING OF MULTILAYER NEURAL NETWORKS WITH THRESHOLD ACTIVATION FUNCTIONS FOR PATTERN CLASSIFICATION

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IFAC Proceedings Volumes 2006年第3期39卷 523-528页

作者： M.P. Batura V.A. Ptitchkin Belarusian State University of Informatics and Radioelectronics 6 P. Brovky Str. 220027 Minsk Belarus

Application of multi-layer neural networks with threshold activation functions is proposed for classification of patterns. For networks learning, using of back-propagation algorithm is proposed. Modification of this algorithm is considered for choosing of initial values of parameters and for learning of neural networks of before-mentioned type.

关键词： Neural networks backpropagation algorithm system analysis threshold functions linearization approximate analysis

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Biometric Retina Identification Based on Neural Network

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Procedia Computer Science 2016年 102卷 26-33页

作者： Fahreddin Sadikoglu Selin Uzelaltinbulat Department of Electrical and Electronic Engineering Near East University P.O.Box:99138 Nicosia North Cyprus Mersin 10 Turkey Department of Computer Engineering Near East University P.O.Box:99138 Nicosia North Cyprus Mersin 10 Turkey

In this paper the design of recognition system for retinal images using neural network is considered. Retina based recognition is perceived as the most secure method for identification of an identity used to distinguish individuals. The retina recognition stages including retina image acquisition, feature extraction and classification of the features are discussed. The structure of the neural network based retina identification is presented. Training of neural network based recognition system is performed using backpropagation algorithm. The structure of neural networks used for retina recognition and its learning algorithm are described. The implementation of recognition system has been done using MATLAB package.

关键词： Neural network retina recognition backpropagation algorithm

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Neural Prediction of a Hydrocarbon Degradation Process in Biopiles 1

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IFAC Proceedings Volumes 2004年第3期37卷 287-292页

作者： R. De la Torre Sánchez I. Baruch J. Barrera-Cortés CINVESTAV-IPN Department of Biotechnology and Bioengineering Av. Instituto Politécnico Nacional No 2508 Col. San Pedro Zacatenco México D.F. C.P. 07360 Fax: (+52-5)747-33-13 CINVESTAV-IPN Department of Automatic Control Av. Instituto Politécnico Nacional No 2508 Col. San Pedro Zacatenco México D.F. C.P. 07360 Fax: (+52-5)747-33-13

This paper proposes the use of a Recurrent Neural Network model (RNN) for modeling a hydrocarbon degradation process carried out in a biopile system. The proposed RNN model has seven inputs, five outputs and twelve neurons in the hidden layer, with global and local feedbacks. The learning algorithm is a modified version of the backpropagation through time. The approximation and generalization error is below 2%. The learning was performed in 131 epochs, 56 iterations each one

关键词： Neural networks backpropagation algorithm biotechnology hydrocarbon degradation biopile system

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New learning automata based algorithms for determining the number of input weights of hidden layers in a multi layer neural networks

Amirkabir (Journal of Science and Technology)

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Amirkabir (Journal of Science and Technology) 2004年第58 A期15卷 286-319页

作者： Mashoufi, B. Mevbodi, M.R. Motamedi, Sayed A. Menhaj, M.B. Electrical Engineering Department Uremia University Iran Computer Engineering Department Amirkabir University of Technology Iran Electrical Engineering Department Amirkabir University of Technology Iran

backpropagation algorithm performs gradient descent only in the weight space of a network with fixed topology. The Number of layers, Neurons and network weights have important influence on network performance. So algorithms that can find appropriate network architecture automatically are thus highly desirable. Researchers have proposed different algorithms for determining optimum size of neural networks. Meybodi and Beigy introduced the first learning automata based algorithms, called WSA algorithm. This algorithm by turning off the unimportant weights, not only reduces network complexity but also increases network generalization ability. At the beginning, all weights of the network are on and contribute to learning. The on weights whose absolute values are less than a threshold value, are penalised and those whose absolute value are larger than another threshold value, are rewarded. The on weights, whose absolute values lie between these two threshold values, neither rewarded, nor penalized. By choosing optimum values for these values we can obtain, networks with minimum number of weights which can learn training patterns with acceptable error and generalization ability. In this paper we introduce a new learning automata based algorithm, called AWSA for adaptation of parameters of WSA algorithm. Also, a new algorithm called MWSA is introduced to determine important weights in the multi layer neural networks. These algorithms are applied to number of problems such as recognition of English number. Persian printed numbers recognition, second order discrete time nonlinear function approximation and Persian phoneme recognition. The results obtained show that the proposed algorithms have better performance than other existing algorithms.

关键词： backpropagation algorithm Learning Automata Multi layer Neural Networks Neural Networks Engineering Neural Networks Topology

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Bank Credit Authorization Using Fuzzy Expert System

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Procedia Computer Science 2016年 102卷 659-662页

作者： Mustafa Menekay Department of International Business Near East University P.O.BOX:99138 Nicosia North Cyprus Mersin 10 Turkey

Companies Financial and economic performance is determined by analyzing their financial statements, that includes such important information as growth in sales, return to stockholders, profit margins, return on equity and credit risks. This information is useful for investors, creditors and other external users. To analyze financial condition of company and make decision need more professionalism and good economical and financial background from managers. In many cases financial manager make decision in condition of uncertainty. Economical instability, political situation needs many companies to work in condition of uncertainty, fuzziness of information. In this paper we take a closer look how information in the financial statement can be combined, analyzed and used by fuzzy expert systems to support many important financial decisions in credit analysis and authorization. This is achieved by the development of knowledge base using knowledge of experienced specialists, human experts in this field. In expert system knowledge base is combined with inference engine to make final decision.

关键词： Neural network retina recognition backpropagation algorithm neural network

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On-Line Training with Guide Data: Shall We Select the Guide Data Randomly or Based on Cluster Centers?

On-Line Training with Guide Data: Shall We Select the Guide ...

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IEEE Symposium Series on Computational Intelligence

作者： Yuya Kaneda Qiangfu Zhao Yong Liu Dept. of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Fukushima Japan

ISBN: (纸本)9781509042418

To retrain an existing multilayer perceptron (MLP) on-line using newly observed data, it is necessary to incorporate the new information while preserving the performance of the network. This is known as the "plasticity-stability" problem. For this purpose, we proposed an algorithm for on-line training with guide data (OLTA-GD). OLTA-GD is good for implementation in portable/wearable computing devices (P/WCDs) because of its low computational cost, and can make us more independent of the internet. Results obtained so far show that, in most cases, OLTA-GD can improve an MLP steadily. One question in using OLTA-GD is how we can select the guide data more efficiently. In this paper, we investigate two methods for guide data selection. The first one is to select the guide data randomly from a candidate data set G, and the other is to cluster G first, and select the guide data from G based on the cluster centers. Results show that the two methods do not have significant difference in the sense that both of them can preserve the performance of the MLP well. However, if we consider the risk of "instantaneous performance degradation", random selection is not recommended. In other words, cluster center-based selection can provide more reliable results for the user during on-line training.

关键词： Decision Boundary Making Multilayer Perceptron backpropagation algorithm On-Line Learning Guide Data

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Off-Line Handwritten Character Recognition using Features Extracted from Binarization Technique

Off-Line Handwritten Character Recognition using Features Ex...

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2013 AASRI Conference on Intelligent Systems and Control（ISC 2013）

作者： Amit Choudhary Rahul Rishi Savita Ahlawat Maharaja Surajmal Institute UIET Maharshi Dayanand University Maharaja Surajmal Institute of Technology

The choice of pattern classifier and the technique used to extract the features are the main factors to judge the recognition accuracy and the capability of an Optical Character Recognition（OCR） system. The main focus of this work is to extract features obtained by binarization technique for recognition of handwritten characters of English language. The recognition of handwritten character images have been done by using multi-layered feed forward artificial neural network as a classifier. Some preprocessing techniques such as thinning, foreground and background noise removal, cropping and size normalization etc. are also employed to preprocess the character images before their classification. Very promising results are achieved when binarization features and the multilayer feed forward neural network classifier is used to recognize the off-line cursive handwritten characters.

关键词： OCR Binarization Feature Extraction Character Recognition backpropagation algorithm Neural Network

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