Essay --

1.0 introThe brain is composed of billions of tiny neurons all combined to create a hierarchy of complex engagements. Much is unknown about intelligence and our concord and perception of intelligence is shaping the way in which we in the twenty- foremost century are creating computer ground bright neural networks. An intelligent system is able to retract nurture from its environment and comprehend without introductory knowledge of the information the litigate, reason about the relationships between variables contained in the information and learn about the process and its operating conditions without human input signal. A computational approach to network dynamics foc employments on the networks ability to think logically, process data and react to changes in the data which can lead to prospective evolution of the network. Traditional rule based computational techniques failed to meet the requirements of search, optimisation and machine learning in large biological and ind ustrial systems and and so had to evolve which shaped the route in which computational intelligence had interpreted in the 21st century. A network is said to be computationally intelligent if it can deal with low level data epitome such as small numerical data has pattern experience components. The main emphasis on neural networks and computationally based network systems was to come up with a learning algorithm that could be apply to increase the intelligence of any given system. Fuzzy logical system was first proposed by prof Lotfi Zadeh in1969 in the University of California Berkley. He created Fuzzy logic to define between data by using partial arrange membership rather than crisp set membership or non-membership. Professor Zadeh explained that people do not need precise numerical inform... ...dimension of the figure memories where the network stores all memories within a stable state. 3.0 Fuzzy logical system Systems Fuzzy Neural Network3.1 What Is Fuzzy Logic?Fuzzy Log ic is a problem solving methodology that lends itself to implementation in a range of systems and can be implemented into networks. It allows an accurate outcome based on vague, ambiguous, imprecise input information. Fuzzy Logic is mainly utilise for control situations although it can be use over a figure of scenarios in situation based computing making it ideal for use within Neural Networks and they require a wide range of input variations. Fuzzy Logic processes user defined rules and therefore it can be readily modified to improve network performance, it can be used to model and control nonlinear data that would beforehand be insufferable model mathematically. 3.2 Crisp Sets and Fuzzy Sets