Volume 7, Issue 1 (3-2015)                   IJICTR 2015, 7(1): 41-51 | Back to browse issues page

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Sheikhan M, Abbasnezhad Arabi M, Gharavian D. Connection Optimization of a Neural Emotion Classifier Using Hybrid Gravitational Search Algorithms . IJICTR. 2015; 7 (1) :41-51
URL: http://ijict.itrc.ac.ir/article-1-108-en.html
1- Electrical Engineering Department Islamic Azad University-South Tehran Branch Tehran, Iran
2- Electrical Engineering Department Shahid Beheshti University Tehran, Iran
Abstract:   (1440 Views)
Artificial neural network is an efficient model in pattern recognition applications, but its performance is heavily dependent on using suitable structure and connection weights. This paper presents a hybrid heuristic method for obtaining the optimal weight set and architecture of a feedforward neural emotion classifier based on Gravitational Search Algorithm (GSA) and its binary version (BGSA), respectively. By considering various features of speech signal and concatenating them to a principal feature vector, which includes frame-based Mel frequency cepstral coefficients and energy, a rich medium-size feature set is constructed. The performance of the proposed hybrid GSA-BGSA-neural model is compared with the hybrid of Particle Swarm Optimization (PSO) algorithm and its binary version (BPSO) used for such optimizations. In addition, other models such as GSA-neural hybrid and PSO-neural hybrid are also included in the performance comparisons. Experimental results show that the GSA-optimized models can obtain better results using a lighter network structure.
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Type of Study: Research | Subject: Information Technology

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