An Optimized Neural Network with Inertia Weight Variation of PSO for the detection of Autism
Christina Jayakumaran, J. Dhalia Sweetlin · 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE) · 2020
Neural Networks is applied for solving diverse number of problems involving classification, clustering and prediction of outcomes that forms the basis for resolving Artificial Intelligence related problems. Particle Swarm Optimization is a widely used algorithm to enhance the effectiveness of neural networks. As a part of this article, a new proposition of varying the inertia weight component during run - time of PSO Iterations is being introduced so as to avoid premature convergence in hybrid - PSO algorithm. The experimental results are compared against the feedforward network with random weights and feedforward network with optimised weights using PSO. The results show a significant increase in the performance.