【實用教育app】Neural Network Fuzzy Systems-1|最夯免費app

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

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

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