Evaluation Model of Bridges Health State Based on DHNN

2014 ◽  
Vol 1079-1080 ◽  
pp. 207-211
Author(s):  
Min He ◽  
Rui Guang Hu ◽  
Shi Le ◽  
Liang Chen
Keyword(s):  
Neural Networks ◽  
Associative Memory ◽  
Evaluation Model ◽  
Memory Capacity ◽  
Iterative Learning ◽  
Health Evaluation ◽  
Health State ◽  
Outer Product ◽  
State Evaluation ◽  
Product Method

Inthis paper, according to the more important ten evaluation indicators, the fourgrades ideal evaluation is established corresponding to the level of healthstate of bridges. Combined with associative memory capacity of discreteHopfield neural networks, a new health state evaluation of bridges ispresented. Five bridges is evaluated by the model, the network connectionweights is obtained by iterative learning using the outer product method. Thesimulation results shows that the health evaluation model can evaluate thehealth state of bridges fast, accurately and intuitively.

Health State Evaluation of Continuous Girder Bridge Based on Fuzzy Analytic Hierarchy Process

2012 ◽  
Vol 178-181 ◽  
pp. 2285-2289
Author(s):  
Hai Tao Li
Keyword(s):  
Analytic Hierarchy Process ◽  
Technical State ◽  
Fuzzy Analytic Hierarchy Process ◽  
Health Evaluation ◽  
Health State ◽  
Fuzzy Evaluation ◽  
Analytic Hierarchy ◽  
State Evaluation ◽  
Degree Of Membership ◽  
Hierarchy Process

Based on fuzzy analytic hierarchy process, The model of bridge health evaluation is established using the quantification relations between the bridge technical state evaluation grade and degree of membership function of bridge health evaluation, making use of the computed result of various index of degree of membership value and weight, obtains all levels of fuzzy evaluation collection. According to the maximum membership principles to evaluate the technical state grade of bridge structure the corresponding level, and with its result to instruct the decision-making of bridge maintenance and strengthening.

Some Properties on the Capability of Associative Memory for Higher Order Neural Networks

2016 ◽  
pp. 57-86
Author(s):  
Hiromi Miyajima ◽  
Shuji Yatsuki ◽  
Noritaka Shigei ◽  
Hirofumi Miyajima
Keyword(s):  
Neural Networks ◽  
Associative Memory ◽  
Memory Capacity ◽  
Higher Order ◽  
Higher Order Neural Networks ◽  
Theoretical Results

Higher order neural networks (HONNs) have been proposed as new systems. In this paper, we show some theoretical results of associative capability of HONNs. As one of them, memory capacity of HONNs is much larger than one of the conventional neural networks. Further, we show some theoretical results on homogeneous higher order neural networks (HHONNs), in which each neuron has identical weights. HHONNs can realize shift-invariant associative memory, that is, HHONNs can associate not only a memorized pattern but also its shifted ones.

Some Properties on the Capability of Associative Memory for Higher Order Neural Networks

2016 ◽  
pp. 391-419
Author(s):  
Hiromi Miyajima ◽  
Shuji Yatsuki ◽  
Noritaka Shigei ◽  
Hirofumi Miyajima
Keyword(s):  
Neural Networks ◽  
Associative Memory ◽  
Memory Capacity ◽  
Higher Order ◽  
Higher Order Neural Networks ◽  
Theoretical Results

Higher order neural networks (HONNs) have been proposed as new systems. In this paper, we show some theoretical results of associative capability of HONNs. As one of them, memory capacity of HONNs is much larger than one of the conventional neural networks. Further, we show some theoretical results on homogeneous higher order neural networks (HHONNs), in which each neuron has identical weights. HHONNs can realize shift-invariant associative memory, that is, HHONNs can associate not only a memorized pattern but also its shifted ones.

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