habib dhahri's Publications List

Designing Beta Basis Function Neural Network for Optimization Using Artificial Bee Colony (ABC)

2012-08-26T09:49:28Z

This paper presents an application of swarm intelligence technique namely Artificial Bee Colony (ABC) to design the design of the Beta Basis Function Neural Networks (BBFNN). The focus of this research is to investigate the new population metaheuristic to optimize the Beta neural networks parameters. The proposed algorithm is used for the prediction of benchmark problems. Simulation examples are a...

H Dhahri, A M Alimi, A Abraham (2012) Neural Networks (IJCNN), The 2012 International Joint Conference on : 1-7

Hierarchical multi-dimensional differential evolution for the design of beta basis function neural network

2012-08-26T10:32:26Z

This paper proposes a hierarchical multi-dimensional differential evolution (HMDDE) algorithm, which is an automatic computational frame work for the optimization of beta basis function neural network (BBFNN) wherein the neural network architecture, weights connection, learning algorithm and its parameters are adapted according to the problem. In the HMDDE-designed neural network, the number of in...

H Dhahri, A M Alimi, A Abraham (2012) Neurocomputing 97: 131-140

Hierarchical Particle Swarm Optimization for the Design of Beta Basis Function Neural Network

2012-08-26T09:45:08Z

A novel learning algorithm is proposed for non linear modeling and identification by the use of the beta basis function neural network (BBFNN). The proposed method is a hierarchical particle swarm optimization (HPSO). The objective of this paper is to optimize the parameters of the beta basis function neural network (BBFNN) with high accuracy. The population of HPSO forms multiple beta neural netw...

H Dhahri, A M Alimi, A Abraham (2012) Intelligent Informatics : 193-205

Opposition-based differential evolution for beta basis function neural network

2012-08-26T09:51:36Z

Many methods for solving optimization problems, whether direct or indirect, rely upon gradient information and therefore may converge to a local optimum. Global optimization methods like Evolutionary algorithms, overcome this problem although these techniques are computationally expensive due to slow nature of the evolutionary process. In this work, a new concept is investigated to accelerate the ...

H Dhahri, A M Alimi (2010) Evolutionary Computation (CEC), 2010 IEEE Congress on : 1-8

Opposition-based particle swarm optimization for the design of beta basis function neural network

2012-08-26T09:52:52Z

Many methods for solving optimization problems, whether direct or indirect, rely upon gradient information and therefore may converge to a local optimum. Global optimization methods like Evolutionary algorithms, overcome this problem although these techniques are computationally expensive due to slow nature of the evolutionary process. In this work, a new concept is investigated to accelerate the ...

H Dhahri, A M Alimi (2010) Neural Networks (IJCNN), The 2010 International Joint Conference on : 1-8

The modified particle swarm optimization for the design of the beta basis function neural networks

2012-08-26T09:53:48Z

This paper proposes and describes an effective utilization of the heuristic optimization. The focus of this research is on a hybrid method combining two heuristic optimization techniques; Differential evolution algorithms (DE) and particle swarm optimization (PSO), to train the beta basis function neural network (BBFNN). Denoted as PSO- DE, this hybrid technique incorporates concepts from DE and P...

H Dhahri, A M Alimi, F Karray (2008) Evolutionary Computation, 2008. CEC 2008.(IEEE World Congress on Computational Intelligence). IEEE Congress on : 3874-3880

Designing beta basis function neural network for optimization using particle swarm optimization

2012-08-26T09:55:01Z

Many methods for solving optimization problems, whether direct or indirect, rely upon gradient information and therefore may converge to a local optimum. Global optimization methods like evolutionary algorithms, overcome this problem. In this work it is investigated how to construct a quality BBF network for a specific application can be a time-consuming process as the system must select both a su...

H Dhahri, A M Alimi, F Karray (2008) Neural Networks, 2008. IJCNN 2008.(IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on : 2564-2571

Automatic Selection for the Beta Basis Function Neural Networks

2012-08-26T09:46:29Z

In this paper, we propose a differential evolution algorithm based design for the beta basis function neural network. The differential Evolution algorithm has been used in many practical cases and has demonstrated good convergences properties. The differential evolution is used to evolve the beta basis function neural networks topology. Compared with the traditional genetic algorithm, the combined...

H Dhahri, A Alimi (2008) Nature Inspired Cooperative Strategies for Optimization (NICSO 2007) : 461-474

The modified differential evolution and the RBF (MDE-RBF) neural network for time series prediction

2012-08-26T09:55:52Z

We develop a modified differential evolution algorithm that produces radial basis function neural network controllers for chaotic systems. This method requires few controlling variables. We examine the result of applying the proposed algorithm to time series prediction, which illustrates the effectiveness of this technique. We apply this algorithm to several computational and real systems includin...

H Dhahri, A M Alimi (2006) Neural Networks, 2006. IJCNN’06. International Joint Conference on : 2938-2943

Hierarchical Learning Algorithm for the Beta Basis Function Neural Network

2012-08-26T09:33:47Z

H Dhahri, M A Alimi (2005) Proc. Third International Conference on Systems, Signals and Devices : SSD05, Sousse, Tunisia SSD :