1.0 IntroductionThe brain is made up of billions of tiny neurons all combining to create a hierarchy of complex networks. Much is unknown about intelligence, and our understanding and perception of intelligence is shaping how we are creating intelligent computer-based neural networks in the 21st century. An intelligent system is able to portray information from its environment and understand the process without prior knowledge of the information, reason about the relationships between the variables contained in the information, and learn the process and its operating conditions without human input. A computational approach to network dynamics focuses on the ability of networks to think logically, process data, and react to changes in the data that can lead to future evolution of the network. Traditional rule-based computational techniques have failed to meet the requirements of search, optimization and machine learning in large biological and industrial systems and thus had to evolve, shaping the path taken by computational intelligence in the 21st century. A network is said to be computationally intelligent if it can handle low-level data analysis, such as small numerical data with pattern recognition components. The main emphasis on neural networks and computation-based network systems was to create a learning algorithm that could be used to increase the intelligence of any system. Fuzzy logic was first proposed by Professor Lotfi Zadeh in 1969 at the University of California, Berkley. He created fuzzy logic to define data using partial set membership rather than precise set membership or non-membership. Professor Zadeh explained that people do not need precise numerical information... half of the paper... size of prototype memories where the network stores all memories in a stable state.3.0 Fuzzy Logic Systems: Fuzzy Neural Network3 .1 What is fuzzy logic? Fuzzy logic is a problem-solving methodology that lends itself to implementation in a wide range of systems and can be implemented in networks. It allows an accurate result based on vague, ambiguous and imprecise input information. Fuzzy logic is primarily used for control situations, although it can be used in a variety of scenarios in situation-based computing, making it ideal for use within neural networks that require a wide range of input variations. Fuzzy logic processes user-defined rules and therefore can be easily modified to improve network performance, it can be used to model and control non-linear data that would previously be impossible to model mathematically.3.2 Crisp Sets and Fuzzy Sets