Abstract
Number of hidden neurons is necessary constant for tuning the neural network to achieve superior performance. These parameters are set manually through experimentation. The performance of the network is evaluated repeatedly to choose the best input parameters.Random selection of hidden neurons may cause underfitting or overfitting of the network. We propose a novel fuzzy controller for finding the optimal value of hidden neurons automatically. The hybrid classifier helps to design competent neural network architecture, eliminating manual intervention for setting the input parameters. The effectiveness of tuning the number of hidden neurons automatically on the convergence of a back-propagation neural network, is verified on speech data. The experimental outcomes demonstrate that the proposed Neuro-Fuzzy classifier can be viably utilized for speech recognition with maximum classification accuracy.
Highlights
Backpropagation Neural Network classifier (BPNN) permits more complex, non-linear relationships of input data to output results [1]
The structure of BPNN classifier is affected by factors like the size of the training set, Input Layer (IL) size, Hidden layer (HL) size, activation function used for learning, Output Layer (OL) size, and so on
This work proposes a technique to estimate the optimal value of hidden neurons using a fuzzy controller based on Mean Squared Error (MSE)
Summary
Backpropagation Neural Network classifier (BPNN) permits more complex, non-linear relationships of input data to output results [1]. The structure of BPNN classifier is affected by factors like the size of the training set, Input Layer (IL) size, Hidden layer (HL) size, activation function used for learning, Output Layer (OL) size, and so on. Out of these factors, we have dealt with Hidden Neurons (HN) in this research work. The fuzzy controller reduces the overhead of training the network repeatedly to find the optimal value of hidden neurons. It avoids the necessity of checking the testing accuracy iteratively[2, 31].
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