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Gradient Descent with Momentum Based backpropagation Neural Network for Selection of Industrial Robot

Gradient Descent with Momentum Based Backpropagation Neural ...

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1st International Conference on Information and Communication Technology for Intelligent Systems (ICTIS)

作者： Nayak, Sasmita Choudhury, B. B. Lenka, Saroj Kumar Govt Coll Engn Dept Mech Engn Kalahandi Odisha India IGIT Dept Mech Engn Sarang Odisha India Mody Univ Dept Informat Technol Lakshmangarh India

ISBN: (纸本)9783319309330;9783319309323

Fast development of industrial robots and its utilization by the manufacturing industries for many different applications is a critical task for the selection of robots. As a consequence, the selection process of the robot becomes very much complicated for the potential users because they have an extensive set of parameters of the available robots. In this paper, gradient descent momentum optimization algorithm is used with backpropagation neural network prediction technique for the selection of industrial robots. Through this proposed technique maximum, ten parameters are directly considered as an input for the selection process of robot where as up to seven robot parameter data be used in the existing methods. The rank of the preferred industrial robot evaluates from the perfectly the best probable robot that specifies the most genuine benchmark of robot selection for the particular application using the proposed algorithm. Moreover, the performance of the algorithms for the robot selection is analyzed using Mean Square Error (MSE), R-squared error (RSE), and Root Mean Square Error (RMSE).

关键词： Industrial robot selection Gradient descent algorithm backpropagation algorithm Neural network

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Performance Prediction of Photovoltaic Arrays 52

Performance Prediction of Photovoltaic Arrays

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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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Predicting Students Yearly Performance using Neural Network: A Case Study of BSMRSTU 5

Predicting Students Yearly Performance using Neural Network:...

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5th International Conference on Informatics, Electronics and Vision (ICIEV)

作者： Sikder, Md. Fahim Uddin, Md. Jamal Halder, Sajal Bangabandhu Sheikh Mujibur Rahman Sci & Technol U Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9781509012695

Students academic performance is the reflection of both academic background and family support. This performance record is critical for the educational institution because they can learn from this to improve their quality. Educational data mining helps to analyze these data and extract information from it. We can determine the status of learners academic performance. For achieving this we can use techniques like decision tree, neural network, classification, data clustering, support vector machine and so on. In this paper, we will predict student's yearly performance in the form of Cumulative Grade Point Average (CGPA) using neural network and compare that with real CGPA. In this regard, a real dataset would be of great importance. We used real dataset from Bangabandhu Sheikh Mujibur Rahman Science and Technology University (BSMRSTU) to perform the prediction.

关键词： Neural Network Prediction backpropagation algorithm

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Guide Data Generation for On-Line Learning of DBM-Initialized MLP

Guide Data Generation for On-Line Learning of DBM-Initialize...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Kaneda, Yuya Zhao, Qiangfu Liu, Yong Univ Aizu Dept Comp Sci & Engn Aizu Wakamatsu Fukushima Japan

ISBN: (纸本)9781509018970

In this paper, we propose a method for generating guide data, and investigate its efficiency and efficacy for on-line learning with guide data. On-line learning in this research updates a learning model initialized by the decision boundary making algorithm proposed by us in our earlier study. The problem is that, if the guide data are not properly generated, on-line learning may require high computational cost in terms of time, and the learning process may not converge to good result. To solve this problem, we propose to use k-means to cluster all candidates of guide data, and use one datum from each cluster as the guide datum. We conducted experiments on several public databases, using different settings, and confirmed the performance of the proposed method. Specifically, if k=5, we can obtain good models with low computational cost through on-line learning.

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

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SOFT COMPUTING BASED FAULT DETECTION & CLASSIFICATION FOR TRANSMISSION LINES

SOFT COMPUTING BASED FAULT DETECTION & CLASSIFICATION FOR TR...

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International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)

作者： Jain, Ankit Moses, Beaulah Vikrant Inst Technol & Management Dept Elect Engn Gwalior India

ISBN: (纸本)9781467399395

This Paper focuses on detecting, classifying and locating faults on electric power transmission lines. Fault detection, fault classification and fault location have been achieved by using artificial neural networks. Feedforward networks have been employed along with backpropagation algorithm for each of the three phases in the Fault location process. Analysis on neural networks with varying number of hidden layers and neurons per hidden layer has been provided to validate the choice of the neural networks in each step. Simulation results have been provided to demonstrate that artificial neural network based methods are efficient in locating faults on transmission lines and achieve satisfactory performances.

关键词： Artificial Neural Networks Feedforward networks backpropagation algorithm Levenberg-Marquardt algorithm

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Biometric retina identification based on neural network 12th

Biometric retina identification based on neural network

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12th International Conference on Application of Fuzzy Systems and Soft Computing (ICAFS)

作者： Sadikoglu, Fahreddin Uzelaltinbulat, Selin Near East Univ Dept Elect & Elect Engn POB 99138 TR-10 North Cyprus Mersin Turkey Near East Univ Dept Comp Engn POB 99138 TR-10 North Cyprus Mersin 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. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： Neural network retina recognition backpropagation algorithm

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Classification Techniques for Texture Images Using Neuroph Studio 5th

Classification Techniques for Texture Images Using Neuroph S...

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5th International Conference on Soft Computing for Problem Solving (SocProS)

作者： Mahani, Priyanka Kulshreshtha, Akanksha Goswami, Anil Kumar Dronacharya Coll Engn Elect & Comp Engn Dept Gurgaon India DRDO DTRL Lab Metcalfe House New Delhi India

ISBN: (纸本)9789811004513;9789811004506

With the widespread scope of image processing in fields such as ground classification, segmentation of satellite images, biomedical surface inspection and content-based image retrieval (CBIR), texture analysis is an important domain with a wide scope. The process of texture analysis comprises the following important steps: texture classification, segmentation and synthesis. Texture is a significant property of images which is difficult to define even though the human eye may recognize it with ease. Texture classification has remained an intangible pattern recognition task despite numerous studies. The prime issue in any texture classification application is how to select the features and which features to consider for representing texture. Another major issue remains the type of metric to be used for comparing the feature vectors. The traditional way of texture classification is to convert the texture image into a vector representing the features using a set of filters. This is followed by classification with a few smoothening steps involved in between. This paper outlines a picture of the basic features of an image as texture, colour and shape which are to be extracted to form the feature vector. The concept of applying Machine learning for the purpose of classification is explored and implemented. The use of Soft Computing Technique-Artificial Neural Networks is illustrated for the purpose of classification. The texture features are extracted using the GLCM method and used as input and fed to the neural network. The algorithm used in training the neural networks is the traditional backpropagation algorithm. The results show which configuration of the multi-layer feedforward architecture is best suited according to our experimental set-up.

关键词： Content-based image retrieval Classification Artificial neural networks backpropagation algorithm

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Optimized Neural Network for Improved Electrocardiogram Beat Classification 6

Optimized Neural Network for Improved Electrocardiogram Beat...

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IEEE 6th International Advance Computing Conference (IACC)

作者： Dash, Sanjit K. Rao, G. Sasibhushana Raajdhani Engn Coll Dept ECE Bhubaneswar Orissa India Andhra Univ Coll Engn Dept ECE Visakhapatnam Andhra Pradesh India

ISBN: (纸本)9781467382861

The performance of neural network as a classifier depends on several factors such as initialization of weights, its architecture, between class imbalance in the dataset, activation function etc. Though a three layered neural network is able to approximate any non-linear function, number of neurons in the hidden layer plays a significant roll in the performance of the classifier. In this study, the importance of the number of hidden layer neurons of neural network is analyzed for the classification of ECG signals. Five different arrhythmias and the normal beat are classified for different number of hidden layer neurons to examine the performance of the classifier. In this study we get the best number as 35. After the training of the neural network with the optimized number of neurons in the hidden layer, we have tested the performance with three different datasets. The average sensitivity, specificity and accuracy achieved is 94.91%, 99.69% and 99.46% respectively.

关键词： Optimized Artificial Neural Network ECG Arrhythmia Multiclass Pattern Classification backpropagation algorithm

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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 (IEEE SSCI)

作者： Kaneda, Yuya Zhao, Qiangfu Liu, Yong Univ Aizu Dept Comp Sci & Engn Aizu Wakamatsu Fukushima Japan

ISBN: (纸本)9781509042401

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 "plasticitystability" 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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Bank credit authorization using fuzzy expert system 12th

Bank credit authorization using fuzzy expert system

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12th International Conference on Application of Fuzzy Systems and Soft Computing (ICAFS)

作者： Menekay, Mustafa Near East Univ Dept Int Business POB 99138 TR-10 North Cyprus Mersin 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. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： Neural network retina recognition backpropagation algorithm neural network

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