scholarly journals Research on improved convolutional wavelet neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jingwei Liu ◽  
Peixuan Li ◽  
Xuehan Tang ◽  
Jiaxin Li ◽  
Jiaming Chen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Radial Basis Function ◽  
Error Rate ◽  
Basis Function ◽  
Activation Function ◽  
Wavelet Neural Network ◽  
Mean Square ◽  
The Mean

AbstractArtificial neural networks (ANN) which include deep learning neural networks (DNN) have problems such as the local minimal problem of Back propagation neural network (BPNN), the unstable problem of Radial basis function neural network (RBFNN) and the limited maximum precision problem of Convolutional neural network (CNN). Performance (training speed, precision, etc.) of BPNN, RBFNN and CNN are expected to be improved. Main works are as follows: Firstly, based on existing BPNN and RBFNN, Wavelet neural network (WNN) is implemented in order to get better performance for further improving CNN. WNN adopts the network structure of BPNN in order to get faster training speed. WNN adopts the wavelet function as an activation function, whose form is similar to the radial basis function of RBFNN, in order to solve the local minimum problem. Secondly, WNN-based Convolutional wavelet neural network (CWNN) method is proposed, in which the fully connected layers (FCL) of CNN is replaced by WNN. Thirdly, comparative simulations based on MNIST and CIFAR-10 datasets among the discussed methods of BPNN, RBFNN, CNN and CWNN are implemented and analyzed. Fourthly, the wavelet-based Convolutional Neural Network (WCNN) is proposed, where the wavelet transformation is adopted as the activation function in Convolutional Pool Neural Network (CPNN) of CNN. Fifthly, simulations based on CWNN are implemented and analyzed on the MNIST dataset. Effects are as follows: Firstly, WNN can solve the problems of BPNN and RBFNN and have better performance. Secondly, the proposed CWNN can reduce the mean square error and the error rate of CNN, which means CWNN has better maximum precision than CNN. Thirdly, the proposed WCNN can reduce the mean square error and the error rate of CWNN, which means WCNN has better maximum precision than CWNN.

A New Radial Basis Function Artificial Neural Network based Recognition for Kurdish Manuscript

2013 ◽  
Vol 4 (4) ◽  
pp. 72-87 ◽  
Author(s):  
Behnam Zebardast ◽  
Isa Maleki
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Recognition Accuracy ◽  
Mean Square ◽  
Recognition Pattern ◽  
Testing Stage ◽  
Radial Basis ◽  
The Mean ◽  
Artificial Neural ◽  
Considerable Discussion

During recent decades, recognizing letters was a considerable discussion for artificial intelligence researchers and recognize letters due to the variety of languages and different approaches have many challenges. The Artificial Neural Networks (ANNs) are framed based on particular application such as recognition pattern and data classification through learning process is configured. So, it is a proper approach to recognize letters. Kurdish language has two popular handwritings based on Arabic and Latin. In this paper, Radial Basis Function (RBF) of ANNs is used to recognize Kurdish-Latin manuscripts. Although, the authors' proposed method is also used to recognize the letters of all Latin languages which include English, Turkish and etc. are used. The authors implement RBF of ANNs in MATLAB environment. In this paper, the efficiency criteria is supposed to minimize the Mean Square Error (MSE) to recognize Kurdish letters and maximize recognition accuracy of Kurdish letters in training and testing stage of RBF of ANNs. The recognition accuracy in training and testing stages are 100% and 96.7742%, respectively.

scholarly journals Subset selection of markers for the genome-enabled prediction of genetic values using radial basis function neural networks

2020 ◽  
Vol 43 ◽  
pp. e46307 ◽  
Author(s):  
Isabela de Castro Sant'Anna ◽  
Gabi Nunes Silva ◽  
Moysés Nascimento ◽  
Cosme Damião Cruz
Keyword(s):  
Neural Networks ◽  
Root Mean Square Error ◽  
Radial Basis Function ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Basis Function ◽  
Stepwise Regression ◽  
Subset Selection ◽  
Mean Square ◽  
Selection Of

This paper aimed to evaluate the effectiveness of subset selection of markers for genome-enabled prediction of genetic values using radial basis function neural networks (RBFNN). To this end, an F1 population derived from the hybridization of divergent parents with 500 individuals genotyped with 1000 SNP-type markers was simulated. Phenotypic traits were determined by adopting three different gene action models – additive, additive-dominant, and epistatic, representing two dominance situations: partial and complete with quantitative traits having a heritability (h2) of 30 and 60%; traits were controlled by 50 loci, considering two alleles per locus. Twelve different scenarios were represented in the simulation. The stepwise regression was used before the prediction methods. The reliability and the root mean square error were used for estimation using a fivefold cross-validation scheme. Overall, dimensionality reduction improved the reliability values for all scenarios, specifically with h2 =30 the reliability value from 0.03 to 0.59 using RBFNN and from 0.10 to 0.57 with RR-BLUP in the scenario with additive effects. In the additive dominant scenario, the reliability values changed from 0.12 to 0.59 using RBFNN and from 0.12 to 0.58 with RR-BLUP, and in the epistasis scenarios, the reliability values changed from 0.07 to 0.50 using RBFNN and from 0.06 to 0.47 with RR-BLUP. The results showed that the use of stepwise regression before the use of these techniques led to an improvement in the accuracy of prediction of the genetic value and, mainly, to a large reduction of the root mean square error in addition to facilitating processing and analysis time due to a reduction in dimensionality.

Prediction of dissolved oxygen in the Mediterranean Sea along Gaza, Palestine – an artificial neural network approach

2009 ◽  
Vol 60 (12) ◽  
pp. 3051-3059 ◽  
Author(s):  
Hossam Adel Zaqoot ◽  
Abdul Khalique Ansari ◽  
Mukhtiar Ali Unar ◽  
Shaukat Hyat Khan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Dissolved Oxygen ◽  
Radial Basis Function ◽  
Mediterranean Sea ◽  
Basis Function ◽  
Data Sets ◽  
Network Approach ◽  
Neural Network Approach ◽  
The Mediterranean

Artificial Neural Networks (ANNs) are flexible tools which are being used increasingly to predict and forecast water resources variables. The human activities in areas surrounding enclosed and semi-enclosed seas such as the Mediterranean Sea always produce in the long term a strong environmental impact in the form of coastal and marine degradation. The presence of dissolved oxygen is essential for the survival of most organisms in the water bodies. This paper is concerned with the use of ANNs — Multilayer Perceptron (MLP) and Radial Basis Function neural networks for predicting the next fortnight’s dissolved oxygen concentrations in the Mediterranean Sea water along Gaza. MLP and Radial Basis Function (RBF) neural networks are trained and developed with reference to five important oceanographic variables including water temperature, wind velocity, turbidity, pH and conductivity. These variables are considered as inputs of the network. The data sets used in this study consist of four years and collected from nine locations along Gaza coast. The network performance has been tested with different data sets and the results show satisfactory performance. Prediction results prove that neural network approach has good adaptability and extensive applicability for modelling the dissolved oxygen in the Mediterranean Sea along Gaza. We hope that the established model will help in assisting the local authorities in developing plans and policies to reduce the pollution along Gaza coastal waters to acceptable levels.

Product Identification Using Image Processing and Radial Basis Function Neural Networks

2015 ◽  
Vol 761 ◽  
pp. 120-124
Author(s):  
K.A.A. Aziz ◽  
Abdul Kadir ◽  
Rostam Affendi Hamzah ◽  
Amat Amir Basari
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Radial Basis Function ◽  
Basis Function ◽  
Network Architecture ◽  
Rbf Neural Networks ◽  
Specific Product ◽  
Product Identification ◽  
Radial Basis

This paper presents a product identification using image processing and radial basis function neural networks. The system identified a specific product based on the shape of the product. An image processing had been applied to the acquired image and the product was recognized using the Radial Basis Function Neural Network (RBFNN). The RBF Neural Networks offer several advantages compared to other neural network architecture such as they can be trained using a fast two-stage training algorithm and the network possesses the property of best approximation. The output of the network can be optimized by setting suitable values of the center and the spread of RBF. In this paper, fixed spread value was used for every cluster. The system can detect all the four products with 100% successful rate using ±0.2 tolerance.

scholarly journals Subset selection of markers for genome-enabled prediction of genetic val-ues using radial basis function neural networks

2018 ◽  
Author(s):  
Isabela de Castro Sant' Anna ◽  
Gabi Nunes Silva ◽  
Moysés Nascimento ◽  
Cosme Damiao Cruz
Keyword(s):  
Neural Networks ◽  
Radial Basis Function ◽  
Basis Function ◽  
Mean Squared Error ◽  
Subset Selection ◽  
Predictive Ability ◽  
Phenotypic Traits ◽  
Radial Basis ◽  
The Mean ◽  
Selection Of

This paper aimed to evaluate the efficiency of subset selection of markers for genome-enabled prediction of genetic values using radial basis function neural networks (RBFNN). For this purpose, an F1 population from hybridization of divergent parents with 500 individuals geno-typed with 1,000 SNP-type markers was simulated. Phenotypic traits were determined by adopting three different gene action models – additive, additive-dominant, and epistasic , com-plying with two dominance situations: partial and complete with quantitative traits admitting heritability (h2) equal to 30 and 60%, each one controlled by 50 loci, considering two alleles per locus, totaling 12 different scenarios. To evaluate the predictive ability of RR_BLUP and the neural networks, a cross-validation procedure with five replicates were trained using 80% of the individuals of the population. Two methods were used: dimensionality reduction and stepwise regression. The square of the correlation between the predicted genomic estimated breeding val-ue (GEBV) and the phenotype value was used to measure predictive reliability. For h2 = 0.3 in the additive scenario, the R2 values were 59% for neural network (RBFNN) and 57% for RR-BLUP, and in the epistatic scenario, R2 values were 50% and 41%, respectively. Additionally, when analyzing the mean-squared error root, the difference in performance between the tech-niques is even greater. For the additive scenario, the estimates were 91 for RR-BLUP and 5 for neural networks and, in the most critical scenario, they were 427 for RR-BLUP and 20 for neu-ral network. The results showed that the use of neural networks and variable selection tech-niques allows capturing epistasis interactions, leading to an improvement in the accuracy of pre-diction of the genetic value and, mainly, to a large reduction of the mean square error, which indicates greater genomic value.

APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR PREDICTION OF POSITIVE DYNAMICS FOR TREATMENT OF PATIENTS WITH TUBERCULOSIS

2020 ◽  
Author(s):  
Marina Ermolickaya
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mean Square Error ◽  
Network Model ◽  
Neural Network Model ◽  
Test Sample ◽  
Mean Square ◽  
Tuberculosis Patients ◽  
The Mean

Using the RStudio program, a neural network model has been developed that predicts positive dynamics in the treatment of tuberculosis patients in a tuberculosis dispensary hospital. The accuracy of the presented model on the test sample is 99.4%, the mean square error (MSE) is 0.013.

Iterative learning radial basis function neural networks control for unknown multi input multi output nonlinear systems with unknown control direction

2019 ◽  
Vol 41 (12) ◽  
pp. 3452-3467 ◽  
Author(s):  
Tarek Bensidhoum ◽  
Farah Bouakrif ◽  
Michel Zasadzinski
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Nonlinear Systems ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Iterative Learning ◽  
Unknown Control Directions ◽  
Control Scheme ◽  
Unknown Control

In this paper, an iterative learning radial basis function neural-networks (RBF NN) control algorithm is developed for a class of unknown multi input multi output (MIMO) nonlinear systems with unknown control directions. The proposed control scheme is very simple in the sense that we use just a P-type iterative learning control (ILC) updating law in which an RBF neural network term is added to approximate the unknown nonlinear function, and an adaptive law for the weights of RBF neural network is proposed. We chose the RBF NN because it has universal approximation capabilities and can approximate any continuous function. In addition, among the advantages of our controller scheme is the fact that it is applicable to deal with a class of nonlinear systems without the need to satisfy the global Lipschitz continuity condition and we assume, only, that the unstructured uncertainty is norm-bounded by an unknown function. Another advantage of the proposed controller and unlike other works on ILC, we do not need any prior knowledge of the control directions for MIMO nonlinear system. Thus, the Nussbaum-type function is used to solve the problem of unknown control directions. In order to prove the asymptotic stability of the closed-loop system, a Lyapunov-like positive definite sequence is used, which is shown to be monotonically decreasing under the control design scheme. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed control scheme.

Sign in / Sign up

Export Citation Format

Share Document