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Neural Network & Fuzzy Systems

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    Neural Network & Fuzzy Systems

    by: faadooengineers.com 2 5

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    DOWNLOAD  ₹60.00


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    This unique 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

    All topics are not listed due to character limit of 4000 characters.

    Tags: percepcalc

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