scholarly journals Numerical Analysis of Modeling Based on Improved Elman Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Shao Jie ◽  
Wang Li ◽  
Zhao WeiSong ◽  
Zhong YaQin ◽  
Reza Malekian
Keyword(s):  
Neural Network ◽  
Memory Effect ◽  
Iteration Step ◽  
Elman Neural Network ◽  
Chebyshev Neural Network ◽  
Class D ◽  
Proposed Model ◽  
Tone Signal ◽  
Hidden Layer ◽  
Hidden Neurons

A modeling based on the improved Elman neural network (IENN) is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE) varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA) with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL) model, Chebyshev neural network (CNN) model, and basic Elman neural network (BENN) model, the proposed model has better performance.

scholarly journals APPROACH TO THE SYNTHESIS OF NEURAL NETWORK STRUCTURE DURING CLASSIFICATION

2020 ◽  
pp. 20-26
Author(s):  
Avazjon R. Marakhimov ◽  
Kabul K. Khudaybergenov
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Feed Forward Neural Network ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Neural Network Structure ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Hidden Neurons

Evaluating the number of hidden neurons necessary for solving of pattern recognition and classification tasks is one of the key problems in artificial neural networks. Multilayer perceptron is the most useful artificial neural network to estimate the functional structure in classification. In this paper, we show that artificial neural network with a two hidden layer feed forward neural network with d inputs, d neurons in the first hidden layer, 2d+2 neurons in the second hidden layer, k outputs and with a sigmoidal infinitely differentiable function can solve classification and pattern problems with arbitrary accuracy. This result can be applied to design pattern recognition and classification models with optimal structure in the number of hidden neurons and hidden layers. The experimental results over well-known benchmark datasets show that the convergence and the accuracy of the proposed model of artificial neural network are acceptable. Findings in this paper are experimentally analyzed on four different datasets from machine learning repository.

Study on Elman Neural Networks Algorithms Based on Factor Analysis

2014 ◽  
Vol 511-512 ◽  
pp. 945-949 ◽  
Author(s):  
Shao Xue Jing ◽  
Wei Kuan Jia
Keyword(s):  
Neural Network ◽  
Factor Analysis ◽  
Computing Time ◽  
Storage Space ◽  
Elman Neural Network ◽  
Actual Problem ◽  
Network Training ◽  
The Index System ◽  
Hidden Layer ◽  
Low Dimensional

When we manipulate high dimensional data with Elman neural network, many characteristic variables provide enough information, but too many network inputs go against designing of the hidden-layer of the network and take up plenty of storage space as well as computing time, and in the process interfere the convergence of the training network, even influence the accuracy of recognition finally. Factor Analysis (FA) concentrates the information that is carried by numerous original indexes which form the index system, and then stores it to the factor, and can according to the precision that the actual problem needs, through controlling the number of the factors, to adjust the amount of the information. In this paper we make full use of the advantages of FA and the properties of Elman neural network structures to establish FA-Elman algorithm. The new algorithm reduces dimensions by FA, and carry on network training and simulation with low dimensional data that we get, which obviously simplifies the network structure, and in the process, improves the training speed and generalization capacity of the Elman neural network.

Quality Prediction Based on the OHIF Elman Neural Network for Key Process

2011 ◽  
Vol 271-273 ◽  
pp. 713-718
Author(s):  
Jie Yang ◽  
Gui Xiong Liu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Process Quality ◽  
Quality Prediction ◽  
Static System ◽  
Quality Model ◽  
Elman Neural Network ◽  
Prediction And Control ◽  
Hidden Layer ◽  
Major Factors

Quality prediction and control methods are crucial in acquiring safe and reliable operation in process quality control. A hierarchical multiple criteria decision model is established for the key process and the weight matrix method stratified is discussed, and then KPCA is used to eliminate minor factors and to extract major factors among so many quality variables. Considering The standard Elman neural network model only effective for the low-level static system, then a new OHIF Elman is proposed in this paper, three different feedback factor are introduced into the hidden layer, associated layer, and output layer of the Elman neural network. In order to coordinate the efficiency of prediction accuracy and prediction, LM-CGD mixed algorithm is used for training the network model. The simulation and experiment results show the quality model can effectively predict the characteristic values of process quality, and it also can identify abnormal change pattern and enhance process control accuracy.

scholarly journals Wavelet-Based Elman Neural Network with the Modified Differential Evolution Algorithm for Forecasting Foreign Exchange Rates

2021 ◽  
Vol 9 (4) ◽  
pp. 421-439
Author(s):  
Renquan Huang ◽  
Jing Tian
Keyword(s):  
Neural Network ◽  
Differential Evolution ◽  
Exchange Rates ◽  
Foreign Exchange ◽  
Differential Evolution Algorithm ◽  
Wavelet Function ◽  
Foreign Exchange Rates ◽  
Elman Neural Network ◽  
Evolution Algorithm ◽  
Hidden Layer

Abstract It is challenging to forecast foreign exchange rates due to the non-linear characters of the data. This paper applied a wavelet-based Elman neural network with the modified differential evolution algorithm to forecast foreign exchange rates. Elman neural network has dynamic characters because of the context layer in the structure. It makes Elman neural network suit for time series problems. The main factors, which affect the accuracy of the Elman neural network, included the transfer functions of the hidden layer and the parameters of the neural network. We applied the wavelet function to replace the sigmoid function in the hidden layer of the Elman neural network, and we found there was a “disruption problem” caused by the non-linear performance of the wavelet function. It didn’t improve the performance of the Elman neural network, but made it get worse in reverse. Then, the modified differential evolution algorithm was applied to train the parameters of the Elman neural network. To improve the optimizing performance of the differential evolution algorithm, the crossover probability and crossover factor were modified with adaptive strategies, and the local enhanced operator was added to the algorithm. According to the experiment, the modified algorithm improved the performance of the Elman neural network, and it solved the “disruption problem” of applying the wavelet function. These results show that the performance of the Elman neural network would be improved if both of the wavelet function and the modified differential evolution algorithm were applied integratedly.

scholarly journals Fruit recognition based on pulse coupled neural network and genetic Elman algorithm application in apple harvesting robot

2020 ◽  
Vol 17 (1) ◽  
pp. 172988141989747 ◽  
Author(s):  
Weikuan Jia ◽  
Shanhao Mou ◽  
Jing Wang ◽  
Xiaoyang Liu ◽  
Yuanjie Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Recognition Rate ◽  
Back Propagation ◽  
Structural Evolution ◽  
Operating Efficiency ◽  
Optimal Method ◽  
Elman Neural Network ◽  
Pulse Coupled Neural Network ◽  
Hidden Neurons ◽  
Harvesting Robot

In order to improve the harvesting efficiency of apple harvesting robot, this article presents an apple recognition method based on pulse coupled neural network and genetic Elman neural network (GA-Elman). Firstly, we use pulse coupled neural network to segment the captured 150 images, respectively, and extract six color features of R, G, B, H, S, and I and 10 shape features of circular variance, density, the ratio of perimeter square to area, and Hu invariant moments of segmented images, and these 16 features are considered as the inputs of Elman neural network. In order to overcome some defects of Elman neural network, such as, trapping local minimum easily and determining the number of hidden neurons difficultly; in this article, genetic algorithm is introduced to optimize it, and new optimization way is designed, that is, the connection weights and number of hidden neurons separate encoding and evolving simultaneously, in the process of structural evolution at the same time the learning of connection weights is completed, and then the operating efficiency and recognition precision of Elman model are improved. In order to get more precision neural network, and avoid the influence of fruit recognition caused by branches or leaves shadow, apple along with branches and leaves is allowed to train. The results of experiments show that compared with the traditional back-propagation, Elman neural network, and other two recognition algorithms of obscured fruit. the genetic Elman neural network algorithm is the optimal method which successful training rate can reach to 100%, recognition rate of overlapping fruit and obscured fruit can reach to 88.67% and 93.64%, respectively, and the total recognition rate reaches to 94.88%.

ON THE CLASSIFICATION CAPABILITY OF A DYNAMIC THRESHOLD NEURAL NETWORK

1994 ◽  
Vol 05 (02) ◽  
pp. 103-114
Author(s):  
CHENG-CHIN CHIANG ◽  
HSIN-CHIA FU
Keyword(s):  
Neural Network ◽  
Upper Bound ◽  
Learning Algorithm ◽  
Dynamic Threshold ◽  
Training Set ◽  
New Type ◽  
Hidden Layer ◽  
Hidden Neurons ◽  
The Given ◽  
Classification Capability

This paper proposes a new type of neural network called the Dynamic Threshold Neural Network (DTNN) which is theoretically and experimentally superior to a conventional sigmoidal multilayer neural network in classification capability, Given a training set containing 4k+1 patterns in ℜn, to successfully learn this training set, the upper bound on the number of free parameters for a DTNN is (k+1)(n+2)+2(k +1), while the upper bound for a sigmoidal network is 2k(n+1)+(2k+1). We also derive a learning algorithm for the DTNN in a similar way to the derivation of the backprop learning algorithm. In simulations on learning the Two-Spirals problem, our DTNN with 30 neurons in one hidden layer takes only 3200 epochs on average to successfully learn the whole training set, while the single-hidden-layer feedforward sigmoidal neural networks have never been reported to successfully learn the given training set even though more hidden neurons are used.

scholarly journals PEMODELAN PENGARUH SENTIMEN PUBLIK DI TWITTER TERKAIT POLITIK TERHADAP KURS RUPIAH DAN INDEKS HARGA SAHAM DENGAN PENDEKATAN MACHINE LEARNING

2021 ◽  
Vol 2020 (1) ◽  
pp. 989-999
Author(s):  
Epan Mareza Primahendra ◽  
Budi Yuniarto
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Big Data ◽  
Least Square ◽  
Support Vector ◽  
Hidden Layer ◽  
Hidden Neurons

Kurs Rupiah dan indeks harga saham (IHS) berpengaruh terhadap perekonomian Indonesia. Pergerakan kurs Rupiah dan IHS dipengaruhi oleh, informasi publik, kondisi sosial, dan politik. Kejadian politik banyak menimbulkan sentimen dari masyarakat. Sentimen tersebut banyak disampaikan melalui media sosial terutama Twitter. Twitter merupakan sumber big data yang jika datanya tidak dimanfaatkan akan menjadi sampah. Pengumpulan data dilakukan pada periode 26 September 2019 - 27 Oktober 2019. Pola jumlah tweets harian yang sesuai dengan pergerakan kurs Rupiah dan IHS mengindikasikan bahwa terdapat hubungan antara sentimen di Twitter terkait situasi politik terhadap kurs Rupiah dan IHS. Penelitian ini menggunakan pendekatan machine learning dengan algoritma Neural Network dan Least Square Support Vector Machine. Penelitian ini bertujuan untuk mengetahui pengaruh sentimen terhadap kurs Rupiah dan IHS sekaligus mengkaji kedua algoritmanya. Hasilnya menjelaskan bahwa model terbaik untuk estimasi IHS yaitu NN dengan 1 hidden layer dan 2 hidden neurons. Modelnya menunjukan bahwa terdapat pengaruh antara sentimen tersebut terhadap IHS karena volatilitas estimasi IHS sudah cukup mengikuti pola pergerakan IHS aktual. Model terbaik untuk estimasi kurs Rupiah yaitu LSSVM. Pola pergerakan estimasi kurs Rupiah cenderung stagnan di atas nilai aktual. Ini mengindikasikan bahwa modelnya masih belum memuaskan dalam mengestimasi pengaruh sentimen publik terhadap kurs Rupiah.

Solving two-dimensional linear partial differential equations based on Chebyshev neural network with extreme learning machine algorithm

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Min Liu ◽  
Muzhou Hou ◽  
Juan Wang ◽  
Yangjin Cheng
Keyword(s):  
Neural Network ◽  
Partial Differential Equations ◽  
Extreme Learning Machine ◽  
Two Dimensional ◽  
Content Type ◽  
Linear Partial Differential Equations ◽  
Chebyshev Neural Network ◽  
Learning Machine ◽  
Hidden Layer ◽  
Linear Pdes

Purpose This paper aims to develop a novel algorithm and apply it to solve two-dimensional linear partial differential equations (PDEs). The proposed method is based on Chebyshev neural network and extreme learning machine (ELM) called Chebyshev extreme learning machine (Ch-ELM) method. Design/methodology/approach The network used in the proposed method is a single hidden layer feedforward neural network. The Kronecker product of two Chebyshev polynomials is used as basis function. The weights from the input layer to the hidden layer are fixed value 1. The weights from the hidden layer to the output layer can be obtained by using ELM algorithm to solve the linear equations established by PDEs and its definite conditions. Findings To verify the effectiveness of the proposed method, two-dimensional linear PDEs are selected and its numerical solutions are obtained by using the proposed method. The effectiveness of the proposed method is illustrated by comparing with the analytical solutions, and its superiority is illustrated by comparing with other existing algorithms. Originality/value Ch-ELM algorithm for solving two-dimensional linear PDEs is proposed. The algorithm has fast execution speed and high numerical accuracy.

scholarly journals Deep Autoencoder-Based Image Compression using Multi-Layer Perceptrons

2020 ◽  
Vol 9 (6) ◽  
pp. 1-6
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Backpropagation Algorithm ◽  
Feed Forward Neural Network ◽  
The Neural Network ◽  
Proposed Model ◽  
Hidden Layer ◽  
Preprocessing Technique ◽  
Block Sizes ◽  
Normalization Process

The Artificial Neural Network is one of the heavily used alternatives for solving complex problems in machine learning and deep learning. In this research, a deep autoencoder-based multi-layer feed-forward neural network has been proposed to achieve image compression. The proposed neural network splits down a large image into small blocks and each block applies the normalization process as the preprocessing technique. Since this is an autoencoder-based neural network, each normalized block of pixels has been initialized as the input and the output of the neural network. The training process of the proposed network has been done for various block sizes and different saving percentages of various kinds of images by using the backpropagation algorithm. The output of the middle-hidden layer will be the compressed representation for each block of the image. The proposed model has been implemented using Python, Keras, and Tensorflow backend.

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