Sultan Noman Qasem

Learn More
In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: Keywords: Radial basis function network Hybrid learning Particle swarm optimization(More)
This paper presents a new multi-objective evolutionary hybrid algorithm for the design of Radial Basis Function Networks (RBFNs) for classification problems. The algorithm, MEPDEN, Memetic Elitist Pareto evolutionary approach based on the Non-dominated Sorting Differential Evolution (NSDE) multi-objective evolutionary algorithm which has been adapted to(More)
This study proposes RBF Network hybrid learning with Particle Swarm Optimization (PSO) for better convergence, error rates and classification results. In conventional RBF Network structure, different layers perform different tasks. Hence, it is useful to split the optimization process of hidden layer and output layer of the network accordingly. RBF Network(More)
This study proposes RBF Network hybrid learning with Particle Swarm Optimization for better convergence, error rates and classification results. In conventional RBF Network structure, different layers perform different tasks. Hence, it is useful to split the optimization process of hidden layer and output layer of the network accordingly. RBF Network hybrid(More)
In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: a b s t r a c t This paper presents a new multiobjective evolutionary algorithm(More)
— In this paper, an adaptive evolutionary multi-objective selection method of RBF Networks structure is discussed. The candidates of RBF Network structures are encoded into particles in Particle Swarm Optimization (PSO). These particles evolve toward Pareto-optimal front defined by several objective functions with model accuracy and complexity.
In this paper, a Differential Evolution (DE) algorithm for solving multiobjective optimization problems to solve the problem of tuning Artificial Neural Network (ANN) parameters is presented. The multiobjective evolutionary used in this study is a Differential Evolution algorithm while ANN used is Three-Term Backpropagation network (TBP). The proposed(More)
—in conventional RBF Network structure, different layers perform different tasks. Hence, it is useful to split the optimization process of hidden layer and output layer of the network accordingly. This study proposes hybrid learning of RBF Network with Particle Swarm Optimization (PSO) for better convergence, error rates and classification results. The(More)
  • 1