[PDF] Top 20 MS_CMAC neural network learning model in structural engineering
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MS_CMAC neural network learning model in structural engineering
... BSTRACT : The present American Institute of Steel Construction specifications use the alignment charts and approximate formulas conveniently to determine some coefficients in design, such as moment ... See full document
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High-order MS_CMAC neural network
... problem. In doing so, they intended to solve the fast size-growing problem and the learning difficulty in currently available types of neural ...networks. In ... See full document
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Improved MS_CMAC neural networks by integrating a simplified UFN model
... 2007 / Accepted: 28 November 2007 / Published online: 13 December 2007 © Springer Science+Business Media, ...Macro_Structure_CMAC ... See full document
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Improved CMAC neural network control scheme
... Introduction: The cerebellar model articulation controller (CMAC) was proposcd by Albus [l]. This neural network is capable of learning noclinear functions extremely qui[r] ... See full document
2
Neural network forecast model in deep excavation
... unsupervised neural network learning algorithms have been developed and explored in a number of various domains 共Adeli and Hung 1995; Haykin ...ANN learning models can ... See full document
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Symbiotic Structure Learning Algorithm for Feedforward Neural-Network-Aided Grey Model and Prediction Applications
... The learning ability of neural networks (NNs) enables them to solve time series prediction ...operate in real time, possess limited memory size, or require online ...the ... See full document
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Unsupervised fuzzy neural network structural active pulse controller
... civil engineering structures subjected to dynamic loading has re- ceived increasing ...control model, termed unsupervised fuzzy neural network structural active pulse ... See full document
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Real-time recurrent learning neural network for stream-flow forecasting
... recurrent neural networks; stream-flow forecasting; rainfall–runoff modelling INTRODUCTION Building a real-time stream-flow forecasting model has always been one of ... See full document
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Shared near neighbours neural network model: a debris flow warning system
... Systems Engineering, National Taiwan University, Taipei, Taiwan, ROC Abstract: The main purpose of this study is to develop a new type of artificial neural network ... See full document
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Single-hidden-layer feed-forward quantum neural network based on Grover learning
... representative neural net- work model that has the activation functions with global ...function in the hidden layer by a local and multiscale wavelet function, followed by a linear ... See full document
7
A Neural-Network-Based Method of Modeling Electric Arc Furnace Load for Power Engineering Study
... artificial neural network is a powerful scheme for function learning and modeling nonlinear ...artificial neural network for modeling time-varying loads may lead to ...accurate ... See full document
1
A neural network approach for structural identification and diagnosis of a building from seismic response data
... depicted in Figure 1, includes an input layer, one or more hidden layers, and an output ...nodes in each layer are connected to each node in the adjacent layer. ... See full document
20
Practical stability issues in CMAC neural network control systems
... [3], [4] (in continuous-time format) and has raised much interests in applying it to various control problems. This scheme does not evolve from traditional control theory, but rath[r] ... See full document
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FUZZY ADAPTIVE LEARNING CONTROL NETWORK WITH ONLINE NEURAL LEARNING
... Given the supervised training data, the proposed learning algorithm first decides whether or not to perform the structure learning based on the fuzzy similarity measure o[r] ... See full document
21
Combining neural network model with seasonal time series ARIMA model
... industry in Taiwan has made steady progress over the past decade, playing a critical supporting role as the foundation to the overall manufacturing industry in ...shown in Fig. 1 and ... See full document
17
Controlling chaos by GA-based reinforcement learning neural network
... reinforcement learning system, called the TDGAR learning system. By the TDGAR learning system, we can train a neural controller for the plant according to a simple ... See full document
14
Fuzzy Clustering Neural Network as Flood Forecasting Model
... The FCNN has a hybrid learning scheme; the unsupervised learning scheme employs fuzzy min-max clustering to extract information from the input data.. The supervised learning scheme [r] ... See full document
16
The Algorithmic Parameters of a Fuzzy Dynamic Learning Neural Network
... 模糊動態學習神經網路之演算參數 曾裕強 1 周念湘 2 摘要 傳統的類神經網路分類器在進行訓練時，係以單一像元單一類別來表示其資訊。因此，對於像元內 類別混合的情形並未加以考慮，致使其分類準確度降低。模糊動態學習神經網路以動態學習神經網路為 ... See full document
12
Fuzzy BP: A Neural Network Model with Fuzzy Inference
... According to the above descriptions, the medians aa', bb', and 2 will intersect at point g whose coordinate is denoted as (x,y). Thus, g is the centroid point of this triangle. The[r] ... See full document
6
Verifying Fuzzy Domain Theories Using a Neural Network Model
... In fuzzy rule verification, we propose a fuzzy rule clustering method to find the inconsistencies, which include redundant rules, conflicting rules and subsumed rules, in[r] ... See full document
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