Neural network fuzzy systems

The app is a complete free handbook of Neuro fuzzy systems or Neural network which cover important topics, notes, materials on the course.

This Neural network App is designed for quick learning, revisions, references at the time of exams and interviews.

This app cover most of related topics and Detailed explanation with all the basics topics.

Some of the topics Covered in the Neural network fuzzy systems 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

All topics are not listed because of character limitations.

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

Features :

* Chapter wise complete Topics

* Rich UI Layout

* Comfortable Read Mode

* Important Exam Topics

* Very simple User Interface

* Cover Most Of Topics

* One click get related All Book

* Mobile Optimized Content

* Mobile Optimized Images

This app will useful for quick reference. The revision of all concepts can be finished within Several hour using this app.

Neuro fuzzy systems or Neural network 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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