Structural Damage Detection Using Soft Computing Method

It is well-known that large neural networks with many unshared weights can be very difficult to train. A neural network ensemble consisting of a number of individual neural networks usually performs better than a complex monolithic neural network. One of the motivations behind neural network ensembles is the divide-and-conquer strategy, where a complex problem is decomposed into different components each of which is tackled by an individual neural network. A promising algorithm for training neural network ensembles is the negative correlation learning algorithm which penalizes positive correlations among individual networks by introducing a penalty term in the error function. A penalty coefficient is used to balance the minimization of the error and the minimization of the correlation. It is often very difficult to select an optimal penalty coefficient for a given problem because as yet there is no systematic method available for setting the parameter. This paper first applies negative correlation learning to the traffic flow prediction problem, and then proposes an evolutionary approach to deciding the penalty coefficient automatically in negative correlation learning. Experimental results on the traffic flow prediction problem will be presented.

To obtain the spatial relationship between three or more pixels in the texture image, bispectrum is choosen to extract texture features of the image, and it contains amplitude information and phase information of the image. Due to some problems in neural network, such as unstable classifier design, configuration, training, the research based on the ensemble of neural networks appears. Compared with a single neural network, an ensemble of neural networks has better fault tolerance and generalisation ability. In this paper, bispectrum is used to extract texture features and the neural network ensembles are used to recognize the texture images. The experimental results demonstrate that the ensemble of BP neural networks can effectively improve correct recognition rate of texture images.

Purpose. At the present stage, network attack detection systems based on the following neural networks are most often offered: multilayer perceptron, Kohonen network or self-organizing map and their combinations. The efficiency problem of two approaches to detect attacks on a computer network using neural network technology based on the normalized data of the open NSL-KDD database is considered. Methodology. As an architectural solution to the network attack detection system, it is proposed to consider the following approaches: based on one neural network determining the attack class (first approach) and an ensemble of five neural networks (second approach), which at the first stage determines the attack category (DoS, Probe, U2R , R2L), and in the second stage, the attack class belonging to a certain category. Findings. Based on the neural networks created in the MatLAB program, a study was conducted of their error on the length of the training sample using various training algorithms: Levenberg-Marquardt; Bayesian Regularization; Scaled Conjugate Gradient with different numbers of hidden neurons (minimum, average and maximum). Certain optimal parameters of neural networks with two approaches were determined. Originality. In the course of conducting experiments with various approaches, the results obtained were: TP (True Positive); FP (False Positive); FN (False Negative); TN (True Negative), based on which the following indicators were calculated for assessing the quality of solutions: correct determination of network attacks; false positives; reliability; accuracy and completeness, which prove the feasibility of using an ensemble of neural networks (second approach). Practical value. On the created neural networks with various approaches, studies were conducted: the operating time of neural networks; errors of the first kind; errors of the second kind. According to the results of the first study, the average operating time of an ensemble of neural networks is 0.92 s, and the operating time of a neural network (according to the first approach) is 2.21 s. According to the results of the second study, the error of the first kind using an ensemble of neural networks is 2.17%, and using the neural network (the first approach) – 7.39%. According to the results of the third study, the error of the second kind using an ensemble of neural networks is 3.91%, and using the neural network (the first approach) – 6.96%, which is confirmed by the efficiency of using an ensemble of neural networks (second approach).

Currently, ensemble approaches based, among other things, on the use of non-network models are powerful tools for solving data analysis problems in various practical applications. An important problem in the formation of ensembles of models is ensuring the synergy of solutions by using the properties of a variety of basic individual solutions; therefore, the problem of developing an approach that ensures the maintenance of diversity in a preliminary pool of models for an ensemble is relevant for development and research. This article is devoted to the study of the possibility of using a method for the probabilistic formation of neural network structures developed by the authors. In order to form ensembles of neural networks, the influence of parameters of neural network structure generation on the quality of solving regression problems is considered. To improve the quality of the overall ensemble solution, using a flexible adjustment of the probabilistic procedure for choosing the type of activation function when filling in the layers of a neural network is proposed. In order to determine the effectiveness of this approach, a number of numerical studies on the effectiveness of using neural network ensembles on a set of generated test tasks and real datasets were conducted. The procedure of forming a common solution in ensembles of neural networks based on the application of an evolutionary method of genetic programming is also considered. This article presents the results of a numerical study that demonstrate a higher efficiency of the approach with a modified structure formation procedure compared to a basic approach of selecting the best individual neural networks from a preformed pool. These numerical studies were carried out on a set of test problems and several problems with real datasets that, in particular, describe the process of ore-thermal melting.

Artificial neural networks apply complex non-linear functions to pattern recognition problems. An ensemble is a 'committee' of neural networks that usually outperforms single neural networks. Bronchiolitis is a common manifestation of viral lower respiratory tract infection in infants and toddlers. To train artificial neural network ensembles to predict the disposition and length of stay in children presenting to the Emergency Department with bronchiolitis. A specifically constructed database of 119 episodes of bronchiolitis was used to train, validate, and test a neural network ensemble. We used EasyNN 7.0 on a 200 Mhz pentium PC with a maths co-processor. The ensemble of neural networks constructed was subjected to fivefold validation. Comparison with actual and predicted dispositions was measured using the kappa statistic for disposition and the Kaplan-Meier estimations and log rank test for predictions of length of stay. The neural network ensembles correctly predicted disposition in 81% (range 75-90%) of test cases. When compared with actual disposition the neural network performed similarly to a logistic regression model and significantly better than various 'dumb machine' strategies with which we compared it. The prediction of length of stay was poorer, 65% (range 60-80%), but the difference between observed and predicted lengths of stay were not significantly different. Artificial neural network ensembles can predict disposition for infants and toddlers with bronchiolitis; however, the prediction of length of hospital stay is not as good.

Context. Image segmentation plays a key role in computer vision. The quality of segmentation is affected by many factors: noise, artifacts, complex shapes of objects. Classical methods cannot always guarantee good success, depending on the quality of the image and the existing noise, they cannot always achieve the desired result. The proposed method uses an ensemble of neural networks, which makes it possible to increase the accuracy and stability of segmentation. Objective. The goal of the work is to develop a new method of combining predictions of neural network ensembles, which can improve segmentation accuracy by combining images of different image sizes. Method. A method is proposed that averages the shapes of objects depicted on prediction masks. A pyramid of images is used to improve segmentation quality, each level of the pyramid corresponds to an increased size of the original image. This approach allows obtaining image characteristics at different levels. For a test image, a prediction is obtained from each neural network in the ensemble, after which a pyramid is built for the image. All pyramid levels are combined into the final image using SAAMC. All obtained final images for each neural network are also combined at the end using SAAMC. The use of an ensemble of neural networks combined with the pyramid method allows for reducing the impact of noise and artifacts on the segmentation results. Results. The use of this method was compared with the usual use of individual neural networks and the ensemble averaging method. The obtained results show that the proposed method outperforms its competitors. Application of the proposed method improved the accuracy and quality of segmentation. Conclusions. The conducted research confirmed the sense of using an ensemble of neural networks and creating a new method of combining predictions. The use of an ensemble of neural networks makes it possible to compensate for the errors and shortcomings of individual neural networks. Using the proposed method can significantly reduce the impact of noise and artifacts on segmentation. Further study and modification of this method will make it possible to further improve the quality of segmentation.

A reasoning and control architecture for vision-guided navigation that makes a robot more humanlike is presented. This system, called NEURO-NAV, discards the more traditional geometrical representation of the environment, and instead uses a semantically richer nonmetrical representation in which a hallway is modeled by the order of appearance of various landmarks and by adjacency relationships. With such a representation, it becomes possible for the robot to respond to commands such as, 'follow the corridor and turn right at the second T junction'. This capability is achieved by an ensemble of neural networks whose activation and deactivation are controlled by a rule-based supervisory controller. The individual neural networks in the ensemble are trained to interpret visual information and perform primitive navigational tasks such as hallway following and landmark detection.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The authors describe a vision-guided mobile robot navigation system, called NEURO-NAV, that is human-like in two senses. The robot can function with non-metrical models of the environment in much the same manner as humans. It does not need a geometric model of the environment. It is sufficient if the environment is modeled by the order of appearance of various landmarks and by adjacency relationships. Also, the robot can response to human-supplied commands. This capability is achieved by an ensemble of neural networks whose activation and deactivation are controlled by a supervisory controller that is rule-based. The individual neural networks in the ensemble are trained to interpret visual information and perform primitive navigational tasks such as hallway following and landmark detection. >

In this paper, we employed neural network ensemble for FPs reduction in detecting lung nodules in chest radiographs. In our scheme, the ensemble consists of four modified forward neural networks, each one of them was trained with the back propagation algorithm to distinct a different type of non-nodules from nodules. The outputs of all the individual neural networks were combined by a modified forward mixing ANN. The performance of our scheme for false positive reduction was evaluated by use of FROC. With neural network ensemble, the false positive rate of CAD scheme1 was reduced for 44% (from 2.86 to 1.6 positives per image), at an overall sensitivity of 60%. We also compared our scheme with other researches. The result demonstrates the superiority of it over other ones. We believe that the proposed method is useful in false positives reduction in the diagnosis of lung nodules in chest radiograph.

Niche-based cooperative co-evolutionary ensemble neural network for classification

Inhaling particulate matter such as PM2.5 can have a hazardous impact on the human health. In order to predict the PM2.5 concentration, Artificial Neural Networks trained with conjugate gradient descent such as Multi Layer Perceptron (MLP), cascade forward neural network, Elman neural network, Radial Basis Function (RBF) neural network and Non-linear Autoregressive model with exogenous input (NARX) along with regression models such as Multiple Linear Regression (MLR) consisting of batch gradient descent, stochastic gradient descent, mini-batch gradient descent and conjugate gradient descent algorithms and Support Vector Regression (SVR) were implemented. In these models, the concentration of PM2.5 was the dependent variable and the data related to concentrations of PM2.5, SO2, O3 and meteorological data including average Maximum Temperature (MAX T), daily wind speed (WS) for the years 2010–2016 in Houston and New York were the independent variables. For the final forecast, several ensemble models of individual neural network predictors and individual regression predictors have been presented.

Neural network ensemble is a learning paradigm where a collection of neural networks is trained for the same task. In this paper, the relationship between the generalization ability of the neural network ensemble and the correlation of the individual neural networks constituting the ensemble is analyzed in the context of combining neural regression estimators, which reveals that ensembling a selective subset of trained networks is superior to ensembling all the trained networks in some cases. Based on such recognition, an approach named GASEN is proposed. GASEN trains a number of individual neural networks at first. Then it assigns random weights to the individual networks and employs a genetic algorithm to evolve those weights so that they can characterize to some extent the importance of the individual networks in constituting an ensemble. Finally it selects an optimum subset of individual networks based on the evolved weights to make up the ensemble. Experimental results show that, comparing with a popular ensemble approach, i.e., averaging all, and a theoretically optimum selective ensemble approach, i.e. enumerating, GASEN has preferable performance in generating ensembles with strong generalization ability in relatively small computational cost. This paper also analyzes the working mechanism of GASEN from the view of error-ambiguity decomposition, which reveals that GASEN improves generalization ability mainly through reducing the average generalization error of the individual neural networks constituting the ensemble.

This paper presents a pruning algorithm i.e., dynamic ensemble pruning algorithm (DEPA) by utilizing the knowledge of and importance of hidden node. The generalization performance of a machine learner depends on how much it avoids the overfilling. The main idea of this algorithm is to reduce the complexity of ensemble networks according to on progress toward training. DEPA emphasizes on avoiding overfilling by dynamically deleting individual neural networks and their hidden nodes starting from a large number of individual neural networks. DEPA has been tested on several standard benchmark problems in machine learning and neural networks, including breast cancer, diabetes and heart disease problems. The experimental results show that DEPA can produce neural network ensembles with good generalization ability.

Melanoma is a rare skin cancer that constitutes only 1% of skin cancer cases. However, its ability to spread to other organs makes it deadliest among the four major cancer types. Early diagnosis of melanoma is essential, as it prevents cancer from spreading to other body parts, therefore significantly reducing mortality rates. In this study, we presented a forward selection‐based ensembling strategy for deep neural networks to aid the diagnosis of melanoma in dermoscopy images. The proposed approach uses an ensemble of neural networks with varying input sizes to effectively capture size‐related various properties of dermoscopy images. To this end, EfficientNet models B3–B7 are used with input resolutions of 256, 384, 512, and 768. Training and validation are carried out in a triple stratified cross‐validation style with folds providing patient isolation, balance in the percentage of classes and balanced patient count distribution. Ensembles are formed by a modified form of forward selection algorithm. Experimental results show that the AUC for classification is increased by 2.01% using the proposed ensembling scheme.

Damage identification in steel girder bridges using modal strain energy-based damage index method and artificial neural network