Asymptotic Properties of a Dynamic Neural System with Asymmetric Connection Weights

In this paper, based on new Lyapunov function, the asymptotic properties of the dynamic neural system with asymmetric connection weights are investigated. Since the dynamic neural system with asymmetric connection weights is more general than that with symmetric ones, the new results are significant in both theory and applications. Specially the new result can cover the asymptotic stability results of linear systems as special cases.

Stability of a Class of Neural Networks with Asymmetric Connection Weights

This paper derives some sufficient conditions for exponential stability for the equilibrium point by dividing the state variables of the system according to the characters of the neural networks. The new conditions are described by some blocks of the interconnection matrix. An example is given to demonstrate the effectiveness of the proposed theory.

Integrated assessment of sea water quality based on BP artificial neural network

In order to carry out an integrated assessment of sea water quality objectively, this paper based on the concept and principle of artificial neural network, generated appropriate training samples for BP artificial neural network model through the method of producing samples to the concentration of various pollution index of sea water quality from the viewpoint of threshold, established the BP artificial neural network model of sea water quality assessment using multi-layer neural network with error back-propagation algorithm. This model was used to assess water environment and obtain sea water quality categories of offshore area in Bohai Bay through calculating. The calculations shown that pollution index in river＇s wet season was higher than that in dry season from 2004 to 2007, and the pollution was particularly serious in 2005 and 2006, but a little better in 2007. The assessed results of cases shown that the model was reasonable in design and higher in generalization, meanwhile, it was common, objective and practical to sea water quality assessment.

从联结主义角度看母语思维在二语习得中的作用

母语思维在二语习得中的作用已经成为二语研究中的一个焦点问题。联结主义学习观证明母语影响是二语习得的重要特征,母语思维在二语习得中既有积极的作用,也有阻碍作用。我们可以利用母语积极的一面,并通过建立新的权重联结来克服其对二语习得的阻碍。

Artificial Neural Network （ANN） and Regression Tree （CART） applications for the indirect estimation of unsaturated soil shear strength parameters

The shear strength parameters of soil （cohesion and angle of internal friction） are quite essential in solving many civil engineering problems. In order to determine these parameters, laboratory tests are used. The main objective of this work is to evaluate the potential of Artificial Neural Network （ANN） and Regression Tree （CART） techniques for the indirect estimation of these parameters. Four different models, considering different combinations of 6 inputs, such as gravel %, sand %, silt %, clay %, dry density, and plasticity index, were investigated to evaluate the degree of their effects on the prediction of shear parameters. A performance evaluation was carried out using Correlation Coefficient and Root Mean Squared Error measures. It was observed that for the prediction of friction angle, the performance of both the techniques is about the same. However, for the prediction of cohesion, the ANN technique performs better than the CART technique. It was further observed that the model considering all of the 6 input soil parameters is the most appropriate model for the prediction of shear parameters. Also, connection weight and bias analyses of the best neural network （i.e., 6/2/2） were attempted using Connec- tion Weight, Garson, and proposed Weight-bias approaches to characterize the influence of input variables on shear strength parameters. It was observed that the Connection Weight Approach provides the best overall methodology for accurately quantifying variable importance, and should be favored over the other approaches examined in this study.

Application of an interpretable artificial neural network to predict the interface strength of a near-surface mounted fiber-reinforced polymer to concrete joint

Accurately estimating the interfacial bond capacity of the near-surface mounted(NSM)carbon fiber-reinforced polymer(CFRP)to concrete joint is a fundamental task in the strengthening and retrofit of existing reinforced concrete(RC)structures.The machine learning(ML)approach may provide an alternative to the commonly used semi-empirical or semi-analytical methods.Therefore,in this work we have developed a predictive model based on an artificial neural network(ANN)approach,i.e.using a back propagation neural network(BPNN),to map the complex data pattern obtained from an NSM CFRP to concrete joint.It involves a set of nine material and geometric input parameters and one output value.Moreover,by employing the neural interpretation diagram(NID)technique,the BPNN model becomes interpretable,as the influence of each input variable on the model can be tracked and quantified based on the connection weights of the neural network.An extensive database including 163 pull-out testing samples,collected from the authors’research group and from published results in the literature,is used to train and verify the ANN.Our results show that the prediction given by the BPNN model agrees well with the experimental data and yields a coefficient of determination of 0.957 on the whole database.After removing one non-significant feature,the BPNN becomes even more computationally efficient and accurate.In addition,compared with the existed semi-analytical model,the ANN-based approach demonstrates a more accurate estimation.Therefore,the proposed ML method may be a promising alternative for predicting the bond strength of NSM CFRP to concrete joint for structural engineers.