Difference between Neural Network And Fuzzy Logic Last Updated : 17 Jul, 2020 Comments Improve Suggest changes Like Article Like Report Neural Network: Neural network is an information processing system that is inspired by the way biological nervous systems such as brain process information. A neural network is composed of a large number of interconnected processing elements known as neurons which are used to solve problems. A neural network is an attempt to make a computer model of the human brain and neural networks are parallel computing devices. The simple diagram of the neural network is as shown below: Fuzzy Logic: The term fuzzy represents the things which are not clear. In the real world many times we find a situation where we can’t determine whether the state is true or false, their fuzzy logic provides very valuable flexibility for reasoning. In this way, we can consider the inaccuracies and uncertainties of any situation. The simple diagram of fuzzy logic is as shown below: Difference between Neural Network And Fuzzy Logic Neural Network Fuzzy Logic This system can not easily modified. This system can easily modified. It trains itself by learning from data set Everything must be defined explicitly. It is complex than fuzzy logic. It is simpler than neural network. It helps to perform predictions. It helps to perform pattern recognition. Difficult to extract knowledge. Knowledge can easily extracted. It based on learning. It doesn't base on learning. Comment More infoAdvertise with us Next Article Difference between Neural Network And Fuzzy Logic B bansal_rtk_ Follow Improve Article Tags : Machine Learning AI-ML-DS Neural Network Practice Tags : Machine Learning Similar Reads Differences Between Bayesian Networks and Neural Networks Bayesian networks and neural networks are two distinct types of graphical models used in machine learning and artificial intelligence. While both models are designed to handle complex data and make predictions, they differ significantly in their theoretical foundations, operational mechanisms, and a 9 min read Difference between Shallow and Deep Neural Networks Neural networks have become a cornerstone of modern machine learning, with their ability to model complex patterns and relationships in data. They are inspired by the human brain and consist of interconnected nodes or neurons arranged in layers. Neural networks can be broadly categorized into two ty 6 min read Difference between a Neural Network and a Deep Learning System Since their inception in the late 1950s, Artificial Intelligence and Machine Learning have come a long way. These technologies have gotten quite complex and advanced in recent years. While technological advancements in the Data Science domain are commendable, they have resulted in a flood of termino 7 min read Difference Between Reinforcement Learning and a Neural Network Reinforcement Learning (RL) focuses on teaching a agent to make decisions by interacting with its environment and learning from the outcomes of its actions whether its a rewards or penalties. The goal is to maximize rewards and minimize penalties. On the other hand Neural Networks (NNs) are inspired 4 min read Difference between Back-propagation and Feed-Forward Neural Network The two key processes associated with neural networks are Feed-Forward and Backpropagation. Understanding the difference between these two is important for deep learning. Feed-Forward is the process where input data passes through the network to produce an output while Backpropagation is the method 4 min read Like