Handbook of Neural network fuzzy systems with diagram, learn a topic in a minute
About Neural network fuzzy systems
Neural network fuzzy systems is an education app developed by Engineering Wale Baba.
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Neural network fuzzy systems has been downloaded 1.8 thousand times. In the last 30 days, the app was downloaded 0 times.
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App Information
Neural network fuzzy systems is FREE to download. The APK download size is 12.16 MB. The latest version available is 16.0. The last update was on August 25, 2025.
Technical Requirements
Neural network fuzzy systems requires Android 2.3.3+ or higher. The app has a content rating of Everyone. The app has been available on Google Play October 2018.
Description
The app is a complete free handbook of Neuro fuzzy systems or Neural network which cover important topics, notes, materials on the course.
This Neural network App is designed for quick learning, revisions, references at the time of exams and interviews.
This app cover most of related topics and Detailed explanation with all the basics topics.
Some of the topics Covered in the Neural network fuzzy systems app are:
1) Register Allocation and Assignment 2) The Lazy-Code-Motion Algorithm 3) Matrix Multiply: An In-Depth Example 4) Rsa topic 1 5) Introduction to Neural Networks 6) History of neural networks 7) Network architectures 8) Artificial Intelligence of neural network 9) Knowledge Representation 10) Human Brain 11) Model of a neuron 12) Neural Network as a Directed Graph 13) The concept of time in neural networks 14) Components of neural Networks 15) Network Topologies 16) The bias neuron 17) Representing neurons 18) Order of activation 19) Introduction to learning process 20) Paradigms of learning 21) Training patterns and Teaching input 22) Using training samples 23) Learning curve and error measurement 24) Gradient optimization procedures 25) Exemplary problems allow for testing self-coded learning strategies 26) Hebbian learning rule 27) Genetic Algorithms 28) Expert systems 29) Fuzzy Systems for Knowledge Engineering 30) Neural Networks for Knowledge Engineering 31) Feed-forward Networks 32) The perceptron, backpropagation and its variants 33) A single layer perceptron 34) Linear Separability 35) A multilayer perceptron 36) Resilient Backpropagation 37) Initial configuration of a multilayer perceptron 38) The 8-3-8 encoding problem 39) Back propagation of error 40) Components and structure of an RBF network 41) Information processing of an RBF network 42) Combinations of equation system and gradient strategies 43) Centers and widths of RBF neurons 44) Growing RBF networks automatically adjust the neuron density 45) Comparing RBF networks and multilayer perceptrons 46) Recurrent perceptron-like networks 47) Elman networks 48) Training recurrent networks 49) Hopfield networks 50) Weight matrix 51) Auto association and traditional application 52) Heteroassociation and analogies to neural data storage 53) Continuous Hopfield networks 54) Quantization 55) Codebook vectors 56) Adaptive Resonance Theory 57) Kohonen Self-Organizing Topological Maps 58) Unsupervised Self-Organizing Feature Maps 59) Learning Vector Quantization Algorithms for Supervised Learning 60) Pattern Associations 61) The Hopfield Network 62) Limitations to using the Hopfield network
All topics are not listed because of character limitations.
Each topic is complete with diagrams, equations and other forms of graphical representations for better learning and quick understanding.
Features : * Chapter wise complete Topics * Rich UI Layout * Comfortable Read Mode * Important Exam Topics * Very simple User Interface * Cover Most Of Topics * One click get related All Book * Mobile Optimized Content * Mobile Optimized Images
This app will useful for quick reference. The revision of all concepts can be finished within Several hour using this app.
Neuro fuzzy systems or Neural network is part of Brain and Cognitive Sciences, AI, computer science, machine learning, electrical, electronics, knowledge engineering education courses and technology degree programs at various universities.
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