Universal approximation using radial-basis-function networks
Neural Computation
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Hybrid identification in fuzzy-neural networks
Fuzzy Sets and Systems - Theme: Learning and modeling
On the use of the weighted fuzzy c-means in fuzzy modeling
Advances in Engineering Software
Linguistic models as a framework of user-centric system modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Neural-network design for small training sets of high dimension
IEEE Transactions on Neural Networks
Conditional fuzzy clustering in the design of radial basis function neural networks
IEEE Transactions on Neural Networks
Blind equalization using a predictive radial basis function neural network
IEEE Transactions on Neural Networks
Boolean Factor Analysis by Attractor Neural Network
IEEE Transactions on Neural Networks
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In this paper, we introduce optimization methods of Polynomial Radial Basis Function Neural Network (pRBFNN) The connection weight of proposed pRBFNN is represented as four kinds of polynomials, unlike in most conventional RBFNN constructed with constant as connection weight Input space in partitioned with the aid of kernel functions and each kernel function is used Gaussian type Least Square Estimation (LSE) is used to estimate the coefficients of polynomial Also, in order to design the optimized pRBFNN model, center value of each kernel function is determined based on C-Means clustering algorithm, the width of the RBF, the polynomial type in the each node, input variables are identified through Particle Swarm Optimization (PSO) algorithm The performances of the NOx emission process of gas turbine power plant data and Automobile Miles per Gallon (MPG) data was applied to evaluate proposed model We analyzed approximation and generalization of model.