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- Habib Dhahri, Adel M. Alimi, Ajith Abraham
- Neurocomputing
- 2012

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… (More)

- Habib Dhahri, Adel M. Alimi, Fakhri Karray
- 2008 IEEE International Joint Conference on…
- 2008

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… (More)

- Habib Dhahri, Adel M. Alimi
- The 2006 IEEE International Joint Conference on…
- 2006

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… (More)

- Souhir Bouaziz, Habib Dhahri, Adel M. Alimi, Ajith Abraham
- Neurocomputing
- 2013

—In this paper, a tree-based encoding method is introduced to represent the Beta basis function neural network. The proposed model called Flexible Beta Basis Function Neural Tree (FBBFNT) can be created and optimized based on the predefined Beta operator sets. A hybrid learning algorithm is used to evolving FBBFNT Model: the structure is developed using the… (More)

- Souhir Bouaziz, Habib Dhahri, Adel M. Alimi
- ICONIP
- 2012

- Habib Dhahri, Adel M. Alimi
- The 2010 International Joint Conference on Neural…
- 2010

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… (More)

- Habib Dhahri, Adel M. Alimi
- IEEE Congress on Evolutionary Computation
- 2010

- Habib Dhahri, Adel M. Alimi, Ajith Abraham
- The 2012 International Joint Conference on Neural…
- 2012

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… (More)

- Habib Dhahri, Adel M. Alimi
- ArXiv
- 2005

– The paper presents a two-level learning method for the design of the Beta Basis Function Neural Network BBFNN. A Genetic Algorithm is employed at the upper level to construct BBFNN, while the key learning parameters :the width, the centers and the Beta form are optimised using the gradient algorithm at the lower level. In order to demonstrate the… (More)

- Habib Dhahri, Adel M. Alimi, Fakhri Karray
- 2008 IEEE Congress on Evolutionary Computation…
- 2008

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… (More)