Determination of Adequate Hidden Neurons in Combo Neural Network Using New Formulation and Fine Tuning with IMGWOA for Enrich Wind-Speed Forecasting Madhiarasan M.1,*, Deepa S.N.2,** 1Research Scholar, Department of Electrical and Electronics Engineering, Anna University, Regional Campus Coimbatore, Coimbatore-641046, Tamil Nadu, India 2Associate Professor, Department of Electrical and Electronics Engineering, Anna University, Regional Campus Coimbatore, Coimbatore-641046, Tamil Nadu, India (*Corresponding author) email id: *mmadhiarasan89@gmail.com
**deepapsg@gmail.com
Abstract This article proposes a new formulation to choose the amount of hidden neurons in combo neural networks, and fine tuning is done by improved modified grey wolf optimisation algorithm (IMGWOA) for wind-speed forecasting. The proposed combo neural network–based wind-speed forecasting model designed by aggregating the forecast values from multiple neural network models such as an ELMAN, recursive radial basis function, multilayer perceptron and improved back propagation network to get the most exact wind-speed forecasting with minimal error. Furthermore, combo neural network performance is enriched by means of fine tuning with IMGWOA. The over-fitting or under-fitting problem is caused because of the random choice of hidden neurons in artificial neural networks. This article presents the solution for both – either over-fitting or under-fitting problems using new formulation. Accurate amount of hidden neurons is determined based on 151 different formulations; these formulations are checked by means of the mean square error. The perfect designing of combo neural network using the new formulation is justified based on the convergence theorem. Proposed formulation performance is compared with other approaches for the determination of hidden neurons in neural networks. The result confirms that the suggested formulations are compact with reduced complexity, effective for determination of the hidden neurons in the combo neural network which minimises error to the least, and accuracy is enriched by the optimisation algorithm (IMGWOA), applied for wind-speed forecasting application. Top Keywords New formulation, Hidden neurons, Generalisation, Comboneural networks, Optimisation, Forecasting, Wind speed. Top |