Neural network and fuzzy systems - medicalbooks.filipinodoctors.org

Show more pictures

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.
Buy From Amazon

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

Buy From Amazon




*If this is not the "Neural network and fuzzy systems" product you were looking for, you can check the other results by clicking this link.  Details were last updated on Oct 23, 2024 18:07 +08.