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Title

TRAINING OF MLP NEURAL NETWORK FOR DATA CLASSIFICATION BY GSA METHOD

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Abstract

 training of the network. Error back propagation is the most usual training method of neural networks that late convergence and stopping in local optimum points are its weakness. New approach in neural networks training is the usage of HEURISTIC ALGORITHMS. This paper suggests a new learning method namely GRAVITATIONAL SEARCH ALGORITHM (GSA) in training of neural network for DATA CLASSIFICATION.GSA method is the latest and the most novel version of swarm intelligence optimization methods. This algorithm is inspired fby the law of Newtonian gravity and mass concept in nature. In this paper, a MLP NEURAL NETWORK is trained for classification of five benchmark data set by GSA method.Also, the proposed method efficiency in training and testing of neural network compared with those of two training methods error back propagation and particle swarm optimization. Final results showed the GSA method extraordinary performance for data correct classification in most of cases. Also, in these experiments the GSA method produced stable results in all of cases. In addition, the run time of GSA method is shorter than that of the PSO.

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    APA: Copy

    DEHBASHIAN, M., & ZAHIRI, S.H.. (2011). TRAINING OF MLP NEURAL NETWORK FOR DATA CLASSIFICATION BY GSA METHOD. NASHRIYYAH-I MUHANDESI-I BARQ VA MUHANDESI-I KAMPYUTAR-I IRAN (PERSIAN), 8(4), 0-0. SID. https://sid.ir/paper/360722/en

    Vancouver: Copy

    DEHBASHIAN M., ZAHIRI S.H.. TRAINING OF MLP NEURAL NETWORK FOR DATA CLASSIFICATION BY GSA METHOD. NASHRIYYAH-I MUHANDESI-I BARQ VA MUHANDESI-I KAMPYUTAR-I IRAN (PERSIAN)[Internet]. 2011;8(4):0-0. Available from: https://sid.ir/paper/360722/en

    IEEE: Copy

    M. DEHBASHIAN, and S.H. ZAHIRI, “TRAINING OF MLP NEURAL NETWORK FOR DATA CLASSIFICATION BY GSA METHOD,” NASHRIYYAH-I MUHANDESI-I BARQ VA MUHANDESI-I KAMPYUTAR-I IRAN (PERSIAN), vol. 8, no. 4, pp. 0–0, 2011, [Online]. Available: https://sid.ir/paper/360722/en

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