Sultan Noman Qasem

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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)
Recently, the problems related to intelligent medical disease diagnosis classification have become one of the important areas of study. Therefore, this paper proposes a new intelligent classifier approach, by using the Three-Term Backpropagation (TBP) network based on the Elitist Multiobjective Genetic Algorithm (MOGA). One of the recent MOGAs is a(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)
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)
The problem of unsupervised and supervised learning is discussed within the context of multi-objective optimization. In this paper, an evolutionary multi-objective selection method of RBF Networks structure is discussed. The candidates of RBF Network structure are encoded into the particles in PSO. Then, they evolve toward Pareto-optimal front defined by(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 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.