Download Self-Adaptive Systems for Machine Intelligence by Haibo He PDF

By Haibo He

ISBN-10: 0470343966

ISBN-13: 9780470343968

ISBN-10: 1118025601

ISBN-13: 9781118025604

This e-book will increase the certainty and alertness of self-adaptive clever platforms; for that reason it's going to in all likelihood profit the long term objective of replicating yes degrees of brain-like intelligence in advanced and networked engineering structures. it is going to offer new techniques for adaptive structures inside of doubtful environments. This will provide a chance to guage the strengths and weaknesses of the present cutting-edge of data, provide upward thrust to new study instructions, and teach destiny execs during this area.

Self-adaptive clever structures have huge purposes from army defense platforms to civilian lifestyle. during this publication, diversified software difficulties, together with development attractiveness, class, picture restoration, and series studying, may be offered to teach the potential of the proposed structures in studying, reminiscence, and prediction. hence, this publication also will supply strength new ideas to many real-world applications.

Chapter 1 advent (pages 1–12):
Chapter 2 Incremental studying (pages 13–43):
Chapter three Imbalanced studying (pages 44–107):
Chapter four Ensemble studying (pages 108–139):
Chapter five Adaptive Dynamic Programming for laptop Intelligence (pages 140–164):
Chapter 6 Associative studying (pages 165–189):
Chapter 7 series studying (pages 190–216):
Chapter eight layout for laptop Intelligence (pages 217–221):

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Example text

Aq . In general, for q base functions one may need to store s combinations and invert q × q matrix ( T ) to update coefficients of the approximating equation. 2). This system has two network structures: a data processing network (DPN) and an information processing network (IPN). The DPN is responsible for the input data space transformation and online dynamic data fitting. The IPN is in charge of the final voting of the results provided by DPN. 1, and will output the fitted values of vni , vui , and vli for information processing elements (IPE).

13) n (1) wf, q (k)xi,q (k), hf (k) = q=1 i = 1, . . 14) 22 INCREMENTAL LEARNING where hf (k) is the f th hidden node input of the neural network and gf (k) is the corresponding output of the hidden node, v(k) is the input to the output node before the sigmoid function, Nh is the number of hidden neurons of the network, and n is the total number of inputs to the network. Here we use the subscript f and q to represent the neuron index in the hidden layer and input layer, respectively. Therefore, one can apply backpropagation to update the weights of the neural network to learn the relationship between the feature space and corresponding distribution function.

1(b)). 1]: Adaptive Incremental Learning Knowledge at time t: — Data chunk, Dt , with m instances: (xi , yi )(i = 1, . . , m), where xi is an instance in the n-dimensional feature space X and yi ∈ Y = {1, . . , C} is the class identity label associated with xi . t ] and i P t = 1. — Distribution function, P t , where P t = [w1t , w2t , . . , wm — A hypothesis, ht , developed by the data based on Dt with P t . New input at time (t + 1): — A new data chunk, Dt+1 , with m may or may not be the same size as m, and can be represented as (xj , yj )(j = 1, .

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Self-Adaptive Systems for Machine Intelligence by Haibo He

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