Collaborative Fuzzy Clustering Mechanism & Data Driven Fuzzy Neural System

In this Research, Fuzzy Logic guideline move system for self-developing Fuzzy neural deduction systems is anticipated. Highlights of proposed strategy, named (CFCM-DDNFS)Collaborative Fuzzy Clustering Mechanism & Data Driven Fuzzy Neural System mechanismwere; (1) Fuzzy guidelines are produced simply by Fuzzy c-means (FCM) and afterward adjusted by the (PCFC)preprocessed synergistic Fuzzy clustering method, and (2) Boundary & Structured learning are accomplished at the same time without choosing the underlying boundaries. The CFCM-DDNFS could implemented to manage enormous information issues by the goodness of the PCFC method, which is fit for managing colossal data-sets while saving the protection and security of data-sets. At first, the whole data-set is composed into two individual data-sets for the PCFC system, where each of the data-set is bunched independently. The information on model factors (bunch focuses) and the lattice of only one divide of data-set across synergistic strategy are sent. CFCM-DDNFS can accomplish consistency within the sight of aggregate information on the PCFC and lift the framework demonstrating procedure by boundary learning capacity of oneself developing neural Fuzzy induction systems (SONFIN). Proposed strategy beats existing strategies for time arrangement forecast issues. Keyword : Fuzzy system,Neural network, Big Data, On-line learning framework,Privacy & security, Time arrangement expectation, Collaborative strategy.