Adaptive Genetic Algorithm for Multilayer RBF Network and its Application on Real Function Approximation

2013 ◽  
Vol 380-384 ◽  
pp. 1166-1169 ◽  
Author(s):  
Yan Hui Wang ◽  
Kun Zhang
Keyword(s):  
Genetic Algorithm ◽  
Computer Simulation ◽  
Real Function ◽  
Function Approximation ◽  
Clustering Algorithm ◽  
Least Squares Method ◽  
Adaptive Genetic Algorithm ◽  
Rbf Networks ◽  
Rbf Network ◽  
Hidden Layer

With the application of adaptive genetic algorithm to the training of multi-layer RBF networks and the optimization of the hidden layer centers and width values and using regularized least squares method, weight vectors is obtained. Computer simulation shows that the precision of real function approximation by this algorithm is much higher than the precision by clustering algorithm for multi-layer RBF networks.

scholarly journals Pattern Classification Based on RBF Networks with Self-Constructing Clustering and Hybrid Learning

2020 ◽  
Vol 10 (17) ◽  
pp. 5886
Author(s):  
Zan-Rong He ◽  
Yan-Ting Lin ◽  
Chen-Yu Wu ◽  
Ying-Jie You ◽  
Shie-Jue Lee
Keyword(s):  
Pattern Classification ◽  
Clustering Algorithm ◽  
Least Squares Method ◽  
Hybrid Learning ◽  
Basis Functions ◽  
Training Data ◽  
Label Pattern ◽  
Classification Problems ◽  
Rbf Networks ◽  
Hidden Layer

Radial basis function (RBF) networks are widely adopted to solve problems in the field of pattern classification. However, in the construction phase of such networks, there are several issues encountered, such as the determination of the number of nodes in the hidden layer, the form and initialization of the basis functions, and the learning of the parameters involved in the networks. In this paper, we present a novel approach for constructing RBF networks for pattern classification problems. An iterative self-constructing clustering algorithm is used to produce a desired number of clusters from the training data. Accordingly, the number of nodes in the hidden layer is determined. Basis functions are then formed, and their centers and deviations are initialized to be the centers and deviations of the corresponding clusters. Then, the parameters of the network are refined with a hybrid learning strategy, involving hyperbolic tangent sigmoid functions, steepest descent backpropagation, and least squares method. As a result, optimized RBF networks are obtained. With this approach, the number of nodes in the hidden layer is determined and basis functions are derived automatically, and higher classification rates can be achieved. Furthermore, the approach is applicable to construct RBF networks for solving both single-label and multi-label pattern classification problems.

scholarly journals An Improved Clustering Method for Detection System of Public Security Events Based on Genetic Algorithm and Semisupervised Learning

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Heng Wang ◽  
Zhenzhen Zhao ◽  
Zhiwei Guo ◽  
Zhenfeng Wang ◽  
Guangyin Xu
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Detection System ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Semisupervised Learning ◽  
Public Security ◽  
Adaptive Genetic Algorithm ◽  
Text Data ◽  
Internet Media

The occurrence of series of events is always associated with the news report, social network, and Internet media. In this paper, a detecting system for public security events is designed, which carries out clustering operation to cluster relevant text data, in order to benefit relevant departments by evaluation and handling. Firstly, texts are mapped into three-dimensional space using the vector space model. Then, to overcome the shortcoming of the traditional clustering algorithm, an improved fuzzy c-means (FCM) algorithm based on adaptive genetic algorithm and semisupervised learning is proposed. In the proposed algorithm, adaptive genetic algorithm is employed to select optimal initial clustering centers. Meanwhile, motivated by semisupervised learning, guiding effect of prior knowledge is used to accelerate iterative process. Finally, simulation experiments are conducted from two aspects of qualitative analysis and quantitative analysis, which demonstrate that the proposed algorithm performs excellently in improving clustering centers, clustering results, and consuming time.

The Prediction of Grounding Grid Corrosion Rate Using Optimized RBF Network

2014 ◽  
Vol 596 ◽  
pp. 245-250
Author(s):  
Chun Feng Song ◽  
Yuan Bin Hou ◽  
Jing Yi Du
Keyword(s):  
Neural Network ◽  
Corrosion Rate ◽  
Ant Colony Algorithm ◽  
Clustering Algorithm ◽  
Rbf Neural Network ◽  
Ant Colony ◽  
Rbf Network ◽  
Grounding Grid ◽  
Ant Colony Clustering ◽  
Hidden Layer

Because the grounding grid corrosion rate has the property of nonlinearity and uncertainty, it is very difficult for us to predict precisely. The approach is proposed that ant colony clustering algorithm is combined with RBF neural network to predict the grounding grid corrosion rate, using ant colony clustering algorithm to get the center of hidden layer neurons. To find the best clustering result, local search is applied in ant colony algorithm. This model has good performance of strong local generalization abilities and satisfying accuracy. At last, it is proved with lots of experiments that the application is fairly effective.

scholarly journals Hardware implementation of radial-basis neural networks with Gaussian activation functions on FPGA

2021 ◽  
Author(s):  
Volodymyr Shymkovych ◽  
Sergii Telenyk ◽  
Petro Kravets
Keyword(s):  
Neural Networks ◽  
Hardware Implementation ◽  
Gaussian Function ◽  
Activation Function ◽  
Rbf Neural Networks ◽  
Activation Functions ◽  
Rbf Network ◽  
Combination Scheme ◽  
Radial Basis ◽  
Hidden Layer

AbstractThis article introduces a method for realizing the Gaussian activation function of radial-basis (RBF) neural networks with their hardware implementation on field-programmable gaits area (FPGAs). The results of modeling of the Gaussian function on FPGA chips of different families have been presented. RBF neural networks of various topologies have been synthesized and investigated. The hardware component implemented by this algorithm is an RBF neural network with four neurons of the latent layer and one neuron with a sigmoid activation function on an FPGA using 16-bit numbers with a fixed point, which took 1193 logic matrix gate (LUTs—LookUpTable). Each hidden layer neuron of the RBF network is designed on an FPGA as a separate computing unit. The speed as a total delay of the combination scheme of the block RBF network was 101.579 ns. The implementation of the Gaussian activation functions of the hidden layer of the RBF network occupies 106 LUTs, and the speed of the Gaussian activation functions is 29.33 ns. The absolute error is ± 0.005. The Spartan 3 family of chips for modeling has been used to get these results. Modeling on chips of other series has been also introduced in the article. RBF neural networks of various topologies have been synthesized and investigated. Hardware implementation of RBF neural networks with such speed allows them to be used in real-time control systems for high-speed objects.

scholarly journals Multimodal Optimization of Permutation Flow-Shop Scheduling Problems Using A Clustering-Genetic-Algorithm-Based Approach

2021 ◽  
Vol 11 (8) ◽  
pp. 3388
Author(s):  
Pan Zou ◽  
Manik Rajora ◽  
Steven Y. Liang
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Flow Shop ◽  
Optimal Schedule ◽  
Flow Shop Scheduling ◽  
Multimodal Optimization ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop ◽  
Global Optima

Though many techniques were proposed for the optimization of Permutation Flow-Shop Scheduling Problem (PFSSP), current techniques only provide a single optimal schedule. Therefore, a new algorithm is proposed, by combining the k-means clustering algorithm and Genetic Algorithm (GA), for the multimodal optimization of PFSSP. In the proposed algorithm, the k-means clustering algorithm is first utilized to cluster the individuals of every generation into different clusters, based on some machine-sequence-related features. Next, the operators of GA are applied to the individuals belonging to the same cluster to find multiple global optima. Unlike standard GA, where all individuals belong to the same cluster, in the proposed approach, these are split into multiple clusters and the crossover operator is restricted to the individuals belonging to the same cluster. Doing so, enabled the proposed algorithm to potentially find multiple global optima in each cluster. The performance of the proposed algorithm was evaluated by its application to the multimodal optimization of benchmark PFSSP. The results obtained were also compared to the results obtained when other niching techniques such as clearing method, sharing fitness, and a hybrid of the proposed approach and sharing fitness were used. The results of the case studies showed that the proposed algorithm was able to consistently converge to better optimal solutions than the other three algorithms.

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