Neural network and fuzzy systems

Features

• This unique free application is for all students of Neural Network & Fuzzy Systems across the world. It covers 149 topics of Neural Network & Fuzzy Systems in detail. These 149 topics are divided in 10 units.
• Each topic is around 600 words and is complete with diagrams, equations and other forms of graphical representations along with simple text explaining the concept in detail.
• The USP of this application is "ultra-portability". Students can access the content on-the-go from any where they like.
• Basically, each topic is like a detailed flash card and will make the lives of students simpler and easier.
• Some of topics Covered in this application 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
• 63) Boltzmann Machines
• 64) Neural Network Models
• 65) Hamming Networks
• 66) Counterpropagation Networks
• 67) RAM-Based Neurons and Networks
• 68) Fuzzy Neurons
• 69) Fuzzy Neural Networks
• 70) Hierarchical and Modular Connectionist Systems
• 71) Neural Networks as a Problem-Solving Paradigm
• 72) Problem Identification and Choosing the Neural Network Model
• 73) Encoding the Information
• 74) The Best Neural Network Model
• 75) Architectures and Approaches to Building Connectionist Expert Systems
• 76) Connectionist Knowledge Bases from Past Data
• 77) Neural Networks Can Memorize and Approximate Fuzzy Rules
• 78) Acquisition of Knowledge
• 79) Destructive Learning
• 80) Competitive Learning Neural Networks for Rules Extraction
• 81) The REFuNN algorithm
• 82) Representing Spatial and Temporal Patterns in Neural Networks
• 83) Pattern Recognition and Classification
• 84) Image Processing
• 85) Speech processing
• 86) MLP for Speech Recognition
• 87) Using SOM for Phoneme Recognition
• 88) Time-Delay Neural Networks for Speech Recognition
• 89) Monitoring
• 90) Connectionist Systems for Diagnosis

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