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Neural network and fuzzy systems

Brand: Ashish Kumar
MPN: com.faadooengineers.free_neuralnetworkandfuzzysyst
Category: App (Science)
Price: $0.00
Availability: Available instantly on compatible devices.
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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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