Exploration and Analysis of Computational Learning Algorithms on the Second Generation Neural Network
Amit Kr. Gupta, Bipin Kumar Tripathi, Vivek Prakash Srivastava · International journal of intelligent engineering and systems · 2018
Computation intelligence is an interesting field having ability to solve many complex problems that are exist in real world.Suitable collaboration of different type of computational learning intelligence techniques viz.fuzzy, evolutionary and neural methods can be more efficient for solving real word complex problems.This paper presents comparative analysis using some intelligence approaches viz.fuzzy c-means clustering(FCM), evolutionary fuzzy clustering with Minkowski distance (EFC-MD) and fusion of EFC-MD with functional modular neural network (EFCMD-FMNN) for determine their efficiencies in real domain (first generation neural network) and complex domain (second generation neural network) both.EFC-MD is introduced for pre-classification, specifying the optimal number of cluster that are assigned in training process.Rather than the Euclidean distance, Minkowski provides the flexibility to algorithm clustering in achieving any size for the cluster keeping the distance matrix in mind.A new approach, hilbert transformation is used for converting real datasets in complex datasets (complex number form) for the computation in complex domain.In this paper initially two existing real domain datasets (wine datasets and monk datasets) is selected and complexities these datasets in complex domain using hilbert approach.Next application of various computational algorithm separately in respective domain to evaluating performance in terms of accuracy.Exploration of these algorithms in complex domain (2-D) investigates improved results and better learning characteristics in the compression of real domain (1-D).