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This paper discusses the method for shirt version intelligent recommendation through combining human body data analysis and apparel loose quantity setting analysis. Firstly, we get the weight of each factor by Analytic Hierarchy Process (AHP) and realize the recommendation of the best shirt shape through the garment size. Then by using the Back Propagation(BP) neural network, we input net body size...
Recently, more neuroscience researches focus on the role of dendritic structure during the neural computation. Inspired by the specified topologies of numerous dendritic trees, we proposed a single neural model with a particular dendritic structure. The dendrites are composed of several branches, and these branches correspond to three distributions in coordinate, which are used to classify the training...
For many years, neural networks have gained gigantic interest and their popularity is likely to continue because of the success stories of deep learning. Nonetheless, their applications are mostly limited to static and not temporal patterns. In this paper, we apply time warping invariant Echo State Networks (ESNs) to time-series classification tasks using datasets from various studies in the UCR archive...
Self-organizing neural network which is an unsupervised learning algorithm is to discover the inherent relationships of data. Such technique has become an important tool for data mining, machine learning and pattern recognition. Most self-organizing neural networks have a difficulty in reflecting data distributions precisely if data distributions are very complex. And meanwhile, it is also hard for...
Stock price fluctuation in stock markets is a very important issue in financial researches. However, the information in stock markets of China is too much to analysis. Fractal theory is an important modern branch of nonlinear science. Neural network has a strong nonlinear approximation ability and self-organizing, adaptive features. Based on fractal theory, the Shanghai integrated index are chosen...
This paper proposed the heart disease diagnosis system using nonlinear ARX (NARX) model. The system uses neural network for model estimation and classification of Normal and several heart diseases based on heart sounds. In classification, a spectrogram was applied to the modeled heart sounds for features extraction and selection. The features were fed to the FFNN and trained using Resilient Backpropagation...
Based on the real data of a Chinese commercial bank's credit card, in this paper, we classify the credit card customers into four classifications by K-means. Then we built forecasting models separately based on four data mining methods such as C5.0, neural network, chi-squared automatic interaction detector, and classification and regression tree according to the background information of the credit...
Determining of the torpedo's service year reasonably, it is an effective way to reduce the military expenses expenditure, and forecast the torpedo economic life. We can forecast the data of exponential use maintenance cost by using the grey metabolism GM(1,1) model. In order to improve the prediction precision, the data was divided into several groups, and prediction residual was modified by using...
Combined with the characteristics of enterprise logistics system, the evaluation index system of enterprise supply logistics system was established using systematic evaluation method. The BP neural network for appraising the performance of enterprise supply logistics system was built. Through the training and simulation of neural network, it is indicated that this evaluation method proves feasible...
At present, research on nonlinear network flows of mobile short message is one hotspot in mobile communications fields. Nonlinear network flows of mobile short message have such essential features varying with time as periodicity, regularity, correlation, randomicity, occasionality. The traditional methods based on linear models are successful relatively in making irregular flow series become more...
Electricity industry is one of the main foundations of each country. Electricity demand growth in developing countries is at its peak so that these countries play an important role in electricity consumption. Since this energy can't be stored in large quantity, forecasting consuming load is a major concern in using electricity energy. This paper has formulated electrify consumption for residential...
This paper presents a neural network (NN) approach for determining the best design combination of product form elements that match a given product value represented by eco-product value (EpV) attributes. Twenty-seven representative office chairs are derived from 100 collected as the experimental samples by using multidimensional scaling and cluster analysis. Moreover, a morphological analysis is applied...
This paper presents a neural network (NN) approach for determining the design combination of product form elements that match a given eco-product value (EPV) and product image. A morphological analysis is used to extract form elements from these sample office chairs. The experimental study identifies 7 office chair design elements and 27 representative office chairs as experimental samples for developing...
Dense-phase zone bed temperature is the key parameters of circulating fluidized bed boiler (CFB) in stable combustion and economic operation. There are great significances on building its bed temperature model. A new method based on PCA and neural network is proposed in this paper, meanwhile, the bed temperature model of CFB is established using this method. Firstly, using principal component analysis...
The failures auto-sensing becomes increasingly essential in the complex systems exploitation. This article consists in working out a system of defects diagnosis based on an artificial intelligence technique which associates fuzzy logic with neural networks. The method is applied to obtain the DAMADICS (Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems). This...
Burr XII distribution plays an important role in reliability modeling, risk analyzing and process capability estimation. However, estimating two parameters of the Burr XII distribution, i.e., c and k, is a complicated task and using conventional methods is not straightforward. In this paper a neural network to estimate Burr XII parameters is presented. The inputs of proposed neural network are skewness...
With the development of economic and continuous improvement of people's living standard, many people have paid more attention to the issue of community security so that a lot of crime prevention systems are installed around the community in recent five years. How to measure the community risk scientifically and make full play to the protection effectiveness on crime prevention system is the complex...
This article established artificial neural networks based on improved ant colony optimization evaluation model for residential performance. Firstly, on the basis of comprehensive analysis of the effects factors of residential building's performance, considering of the advantages of dealing with non-linear object of neural network, the neural network is trained by the sample data. While training neural...
An intelligent method on short-term prediction on water bloom of BP neural network based on rough set and wavelet analysis is proposed in this paper. This method analyzes factors of effecting the outbreak of water bloom, and these many factors which were processed by reduction method based on rough set were used as input information of the prediction model; after analyzing the main input information...
We consider the multi-class classification problem, based on vector observation sequences, where the conditional (given class observations) probability distributions for each class as well as the unconditional probability distribution of the observations are unknown. We develop a novel formulation that combines training with the quality of classification that can be obtained using the 'learned' (via...
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