Time series prediction with single multiplicative neuron model
Applied Soft Computing
Learning of geometric mean neuron model using resilient propagation algorithm
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
The conventional neural networks consisting of simple neuron models have various drawbacks like large training time for complex problems, huge data requirement to train a non linear complex problems, unknown ANN structure, the relatively larger number of hidden nodes required, problem of local minima etc. To make the Artificial Neural Network more efficient and to overcome the above-mentioned problems the new improved generalized neuron model is proposed in this work. The proposed neuron models have both summation (Σ) and product (π) as aggregation function. The generalized neuron models have flexibility at both the aggregation and activation function level to cope with the non-linearity involved in the type of applications dealt with. The training and testing performance of these models have been compared for Short Term Load Forecasting Problem.