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Neural network fuzzy systems

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    The app is a complete free handbook of Neural network, fuzzy systems which cover important topics, notes, materials, news & blogs on the course. Download the App as a reference material & digital book for Brain and Cognitive Sciences, AI, computer science, machine learning, knowledge engineering programs & degree courses. 

    This useful App lists 149 topics with detailed notes, diagrams, equations, formulas & course material, the topics are listed in 10 chapters. The app is must have for all the engineering science students & professionals. 

    The app provides quick revision and reference to the important topics like a detailed flash card notes, it makes it easy & useful for the student or a professional to cover the course syllabus quickly before an exams or interview for jobs. 

    Track your learning, set reminders, edit the study material, add favorite topics, share the topics on social media. 

    You can also blog about engineering technology, innovation, engineering startups,  college research work, institute updates, Informative links on course materials & education programs from your smartphone or tablet or at http://www.engineeringapps.net/. 

    Use this useful engineering app as your tutorial, digital book, a reference guide for syllabus, course material, project work, sharing your views on the blog. 

    Some of the topics Covered in the 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

    Each topic is complete with diagrams, equations and other forms of graphical representations for better learning and quick understanding. 

    Neural network, fuzzy systems 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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