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In this paper, a radial basis neural network (RBFN) for lung cancer screening algorithm is presented. Because of the learning characteristics of the radial basis neural network (RBFN), it has been selected to train the samples, which are the lung cancer examples, and then extracts the internal relations between the pathogenic factors and inducing lung cancer, and eventually it generates empirical...
Local scour around bridge abutment is a time-dependent complex phenomenon encountered world-wide. It is difficult to establish a general empirical model that can be applied to all abutment conditions. In this paper, Radial basis function (RBF) Network has been used to predict the maximum scour depth around bridge abutment. An appropriate model is identified using experimental data from literature...
Training of parameters in RBF neural network, this article proposes an optimization of RBF neural network parameters algorithm, which can overcome the disadvantages of select of data center and weights in RBF neural network. The algorithm process input data normalization and compute network output and hidden layer output angle cosine firstly, a set of data being established as the network center when...
Normalized radial basis function (NRBF) neural network is presented to directly approach the Q-value function and generalize the information learnt by learning agent in continuous space. The action which impacts on environment is the one with maximum output of NRBF in the current state, and generated through Quantum-Behaved Particle Swarm Optimizer based on the current state. The effectiveness of...
Electricity market demands to the power industry in de-regulated form in this paper. The proposed load forecasting using ANN shows the effective risk management plans. This power market is to maintain their effective cost in terms of energy generation, energy purchase and optimization of the switching losses. This creates the need of load forecasting. So in this paper the load forecasting using ANN...
Transmission tower occupies an important position in the event of transmission of electricity. The failure of transmission tower would cause serious economic losses. As a damage identification parameter, variation ratio of curvature mode has a great ability to damage location. In the field of damage location identification on transmission tower, variation ratio of curvature mode achieved good results...
Classification, or supervised learning, is one of the major data mining processes. Protein classification focuses on predicting the function or the structure of new proteins. This can be done by classifying a new protein to a given family with previously known characteristics. There are many approaches available for classification tasks, such as statistical techniques, decision trees and the neural...
Most available forecasters were designed in non-adaptive approach whereby the forecasters' parameters were updated during training phase. Slightly different, this paper introduces an adaptive forecaster built from the Hybrid Radial Basis Function neural network, in which its parameters were updated continuously in real time using new data. To achieve this, two learning algorithms: Adaptive Fuzzy C-Means...
It is well-known that, the pattern recognition performances assigned to RBF neural networks depends a lot by their specific training algorithms, and by the methods used for RBF center selection (e.g., a clustering technique), particularly. Having as starting point the membership of genetic algorithms to the powerful class of global optimization methods, an optimal full-genetic training procedure of...
Expanding mathematical models and forecasting the traffic flow is a crucial case in studying the dynamic behaviors of the traffic systems these days. Artificial Neural Networks (ANNs) are of the technologies presented recently that can be used in the intelligent transportation system field. In this paper, two different algorithms, the Multi-Layer Perceptron (MLP) and the Radial Basis Function (RBF)...
This paper presents the work regarding the implementation of radial basis function algorithm on very high speed integrated circuit hardware description language by using Perceptron learning. Neural Network hardware is usually defined as those devices designed to implement neural architectures and learning algorithms. The radial basis function (RBF) network is a two-layer network whose output units...
In this paper, a novel approach for online motor fault diagnosis is proposed based on artificial neural network (ANN) trained by immune clustering and genetic algorithm (IGA). The IGA is employed to adaptively optimize the structure of the radial basis function neural network (RBFNN). The clonal selection principle is responsible for how the centres will represent the training data set. The immune...
This paper presents a novel algorithm for multiobjective training of Radial Basis Function (RBF) networks based on least-squares and Particle Swarm Optimization methods. The formulation is based on the fundamental concept that supervised learning is a bi-objective optimization problem, in which two conflicting objectives should be minimized. The objectives are related to the empirical training error...
Co-evolutionary algorithms are a class of adaptive search meta-heuristics inspired from the mechanism of reciprocal benefits between species in nature. The present work proposes a cooperative co-evolutionary algorithm to improve the performance of a radial basis function neural network (RBFNN) when it is applied to recognition of handwritten Arabic digits. This work is in fact a combination of ten...
Predictability is an important factor for determining robot motions. This paper presents a model to generate robot motions based on reliable predictability evaluated through a dynamics learning model which self-organizes object features. The model is composed of a dynamics learning module, namely Recurrent Neural Network with Parametric Bias (RNNPB), and a hierarchical neural network as a feature...
Considering of the ill-posed problem in learning process of echo state network(ESN), a new learning algorithm of ESN is proposed based on regularization method. The regularization term provides a stable solution to function approximation with a tradeoff between accuracy and smoothness of the solutions. So the redundant weights of neural network are damped and converged to the zero state. The structure...
In this paper, a novel nonlinear Radial Basis Function Neural Network (RBF-NN) ensemble model based on ν-Support Vector Machine (SVM) regression is presented for financial time series forecasting. In the process of ensemble modeling, the first stage the initial data set is divided into different training sets by used Bagging and Boosting technology. In the second stage, these training sets are input...
Due to the difficulty of determining node number of hidden layers and center, slow learning speed and weaken generalization ability of RBF neural network when input data is generous and complex. A new method based on combined clustering is presented here to determine node number of hidden layer and centers of RBF neural network self-adaptively. In this paper, subtractive clustering is used to cluster...
In this paper, a novel adaptive NN control scheme is proposed for a class of uncertain single-input and single- output(SISO) nonlinear time-delay systems with the lower triangular form. RBF NNs are used to approximate unknown nonlinear functions, then the adaptive NN tracking controller is constructed by combining Lyapunov-Krasovskii functionals and the dynamic surface control(DSC) technique along...
In order to predict price of candidates in acquisition, in this paper, we design a new model of price prediction of target based on radial basis function neural network. The model is trained by the financial data of acquisition market deals which were made in the past. The result of simulation and test indicates that average error of price prediction is percent 12. It is a suggestive reference of...
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