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Abstract
In this paper, we present a learning algorithm for the Elman Recurrent Neural Network (ERNN) based on Biogeography-Based Optimization (BBO). The proposed algorithm computes the weights, initials inputs of the context units and self-feedback coefficient of the Elman network. The method applied for four benchmark problems: Mackey Glass and Lorentz equations, which produce chaotic time series, and to real life classification; iris and Breast Cancer datasets. Numerical experimental results show improvement of the performance of the proposed algorithm in terms of accuracy and MSE eror over many heuristic algorithms.
Highlights
Among the varied types of Neural Nets, Recurrent Neural Network (RNN) is able to forecast the most accurate results (Senjyu et al, 2006)
The results indicate that Biogeography-Based Optimization (BBO) algorithm proves its effectiveness on training Elman Neural Network
Biogeography-Based Optimization (BBO) proposed in (Simon, 2008) is an Evolutionary Algorithm (EA), which is based on migration and immigration to the islands
Summary
Among the varied types of Neural Nets, Recurrent Neural Network (RNN) is able to forecast the most accurate results (Senjyu et al, 2006). Optimisation can be performed by metaheuristic methods (Yao & Kim, 2014) This class of network could be trained with heuristics algorithms because of the inconveniences gradient-based algorithms such as suffering from the local minima. It was three tasks for RNN optimization; weight and bias optimization, architecture optimisation and parameter gradient optimization. Strategies (ES) (Kawada, Yamamoto, & Mada, 2004) and Population Based Incremental Learning (PBIL) (Palafox & Iba, 2012) Both BBO and GA are evolutionary algorithm, but each of them has a specific characteristic.
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