Essay --

1.0 Introduction The brain is composed of billions of tiny neurons all combined to create a hierarchy of complex networks. Much is unknown about intelligence and our understanding and perception of intelligence is shaping the way in which we in the 21st century are creating computer based intelligent neural networks. An intelligent system is able to retract information from its environment and comprehend without prior knowledge of the information the process, reason about the relationships between variables contained in the information and learn about the process and its operating conditions without human input. A computational approach to network dynamics focuses on the networks ability to think logically, process data and react to changes in the data which can lead to future evolution of the network. Traditional rule based computational techniques failed to meet the requirements of search, optimisation and machine learning in large biological and industrial systems and therefore had to evolve which shaped the route in which computational intelligence had taken in the 21st century. A network is said to be computationally intelligent if it can deal with low level data analysis such as small numerical data has pattern recognition components. The main emphasis on neural networks and computationally based network systems was to come up with a learning algorithm that could be used to increase the intelligence of any given system. Fuzzy Logic was first proposed by Professor Lotfi Zadeh in1969 in the University of California Berkley. He created Fuzzy Logic to define between data by using partial set membership rather than crisp set membership or non-membership. Professor Zadeh explained that people do not need precise numerical inform... ...dimension of the prototype memories where the network stores all memories within a stable state. 3.0 Fuzzy Logic Systems: Fuzzy Neural Network 3.1 What Is Fuzzy Logic? Fuzzy Logic 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 used for control situations although it can be used over a variety 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 impossible model mathematically. 3.2 Crisp Sets and Fuzzy Sets