Fashion Color Forecasting by Applying an Improved Back Propagation Neural Network

Fashion color forecasting is one of the most important factors for fashion marketing and manufacturing. Several models have been applied by previous researchers to conduct fashion color forecasting. However, few convincing forecasting systems have been established. A prediction model for fashion color forecasting was established by applying an improved back propagation neural network (BPNN) model in this paper. Successive six-year fashion color palettes, released by INTERCOLOR, were used as learning information for the neural network to develop a reliable prediction model. Colors in the palettes were quantified by PANTONE color system. Additionally, performance of the established model was compared with other GM(1, 1) models. Results show that the improved BPNN model is suitable to predict future fashion color trend.     

Software Maintainability Evaluation Based on Quantitive Model

Software maintainability is one of the most important factors of software quality,but it is seriously difficult to evaluate the maintainability. Without evaluation,it is impossible to control. To estimate software maintainability state,parameter system of software was built up and maintainability state was defined into three states.Thought of application on maintainability evaluation based on hidden Markov chain( HMC) and fuzzy inference was presented.Three-state maintainability estimation model was constructed. To testify the feasibility of the model, a real example of software maintenance activity was carried out and the result from the example validated that the results of this study were applicable.     

Predicting the maintainability of open source software using design metrics

This paper empirically investigates the relationships between 15 design metrics and maintainability of 148 Java open source software. The results show that size and complexity metrics are strongly related to the maintainability of open source software. However, cohesion and coupling, as currently captured by existing metrics, do not seem to have a significant impact on maintainability. When used together, these metrics can predict system maintainability fairly accurately （mean MREs below 30%）.     

Prediction of Enthalpies of Fusion for Divalent Rare Earth Halides Based on Modeling by Artificial Neural Networks and Pattern Recognition

The artificial neural network (ANN) and the pattern recognition were applied to study the correlation of enthalpies of fusion for divalent rare earth halides with their microstructural parameters,such as ionic radius and electronegativity. The model,represented by a back-propagation neural network, was trained with a 12 set of published data for divalent rare earth halides and then was used to predict the unknown ones. Also the criterion equations were presented to determine the enthalpies of fusion for divalent rare earth halides using pattern recognition in mis work. The results from the model in ANN and criterion equations are in very good agreement with reference data.     

An Improved Artificial Neural Network Model for Predicting Silicon Content of Blast Furnace Hot Metal

Based on the skills of initializing weight distribution, adding an impulse in a neural network and expanding the ideal of plural weights, an artificial neural network model with three connection weights between one and another neural unit was established to predict silicon content of blast furnace hot metal. After the neural network was trained in the off-line state on the basis of a large number of practical data of a commercial blast furnace and making many learning patterns, satisfactory testing and simulating results of the model were obtained.     

Study of CNG／diesel dual fuel engine＇s emissions by means of RBF neural network

Great efforts have been made to resolve the serious environmental pollution and inevitable declining of energy resources. A review of Chinese fuel reserves and engine technology showed that compressed natural gas (CNG)/diesel dual fuel engine (DFE) was one of the best solutions for the above problems at present. In order to study and improve the emission performance of CNG/diesel DFE, an emission model for DFE based on radial basis function (RBF) neural network was developed which was a black-box input-output training data model not require priori knowledge. The RBF centers and the connected weights could be selected automatically according to the distribution of the training data in input-output space and the given approximating error. Studies showed that the predicted results accorded well with the experimental data over a large range of operating conditions from low load to high load. The developed emissions model based on the RBF neural network could be used to successfully predict and optimize the emissions performance of DFE. And the effect of the DFE main performance parameters, such as rotation speed, load, pilot quantity and injection timing, were also predicted by means of this model. In resum6, an emission prediction model for CNG/diesel DFE based on RBF neural network was built for analyzing the effect of the main performance parameters on the CO, NOx emissions of DFE. The predicted results agreed quite well with the traditional emissions model, which indicated that the model had certain application value, although it still has some limitations, because of its high dependence on the quantity of the experimental sample data.     

An integrated scheme of neural network and optimal predictive control

An approach of adaptive predictive control with a new structure and a fast algorithm of neural network (NN) is proposed. NN modeling and optimal predictive control are combined to achieve both accuracy and good control performance. The output of nonlinear network model is adopted as a measured disturbance that is therefore weakened in predictive feed-forward control. Simulation and practical application show the effectiveness of control by the proposed approach.     

Evaluation on Stability of Stope Structure Based on Nonlinear Dynamics of Coupling Artificial Neural Network

The nonlinear dynamical behaviors of artificial neural network (ANN) and their application to science and engineering were summarized. The mechanism of two kinds of dynamical processes, i.e. weight dynamics and activation dynamics in neural networks, and the stability of computing in structural analysis and design were stated briefly. It was successfully applied to nonlinear neural network to evaluate the stability of underground stope structure in a gold mine. With the application of BP network, it is proven that the neuro-com-puting is a practical and advanced tool for solving large-scale underground rock engineering problems.     

A computational-intelligence-based optimization of agents'' initial positions in land combat simulation

The optimization of agents' initial properties enables agents to perform their assigned tasks more perfectly. This paper presents an optimizing method using the combination of radial basis function (RBF) neural network and genetic algorithm (GA). In the land combat simulation, the method can ensure that the agents optimized defeat the agents not optimized absolutely. Compared with the optimization based on support vector machines (SVM), the proposed method improves the efficiency more than twenty times, so it suits the cases where the speed as well as performance is required.     

Estimation of froth flotation recovery and collision probability based on operational parameters using an artificial neural network

An artificial neural network and regression procedures were used to predict the recovery and collision probability of quartz flotation concentrate in different operational conditions. Flotation parameters, such as dimensionless numbers (Froude, Reynolds, and Weber), particle size, air flow rate, bubble diameter, and bubble rise velocity, were used as inputs to both methods. The linear regression method shows that the relationships between flotation parameters and the recovery and collision probability of flotation can achieve correlation coefficients (R2) of 0.54 and 0.87, respectively. A feed-forward artificial neural network with 3-3-3-2 arrangement is able to simultaneously estimate the recovery and collision probability as the outputs. In testing stages, the quite satisfactory correlation coefficient of 0.98 was achieved for both outputs. It shows that the proposed neural network models can be used to determine the most advantageous operational conditions for the expected recovery and collision probability in the froth flotation process.     

基于神经网络调制信号识别技术的发展

信号调制样式的自动识别是软件无线电必备的功能之一,基于人工神经网络的识别方法因其较其他方法具有更好的性能受到广泛关注。分析了基于神经网络调制信号识别技术的基本原理,将目前研究的调制信号识别分为基于多层感知器神经网络的调制信号识别和基于径向基函数神经网络的调制信号识别,提出了神经网络调制信号识别技术进一步的研究方向。     

基于神经网络的超声波乳化液质量分数测量

通过反向传播（BP）神经网络及径向基函数（RBF）神经网络，构建了一个实时的超声波乳化液质量分数测量模型，对结果进行了处理和分析，并对比了两者的优缺点．在此基础上，实现了对某些生产过程中乳化液质量分数的检测和控制．     

一种应用神经网络模拟Hash函数的算法

首先介绍了Hash函数的原理以及Hash算法的设计方法,提出了用神经网络模拟Hash函数的思想,并给出了用(径向基函数)RBF网络模拟Hash函数的具体算法。     

基于RBF神经网络的数控车床热误差建模

对于数控车床而言，热误差是其最大的误差源，而其中最困难的是热误差建模．现有BP算法的神经网络模型存在学习收敛速度慢，容易陷入局部极小点的缺点．文中使用径向基函数理论建立了基于RBF神经网络的数控机床热误差数学模型．讨论了RBF网络参数的初始化及学习；给出了两种建模方式的RBF网络建模算例，将其建模性能指标与经典最小二乘法建模指标进行综合对比，可知RBF网络各项指标均优于经典最小二乘方法．最后验证了RBF网络建模的鲁棒性．结果表明：径向基神经网络模型与经典最小二乘线性模型相比，拟合性能更好，预测补偿能力强且建模时间短．     

一种基于顺序图的软件性能分析方法

软件性能工程SPE是一种重要的性能分析方法，它将UML顺序图转化为执行图，进而分析软件的性能是否符合期望的指标。然而软件性能工程（SPT）却未给出顺序图到执行图的具体转化方法，在实际应用特别是自动实现时存在一定的困难，文中通过对模型图的形式化定义，提出了一种顺序图转换为执行图的基本算法，并给出了一种基于顺序图的软件性能评价方法。     

Performance Comparison of Neural Networks for HRTFs Approximation

In order to approach to head-related transfer functions (HRTFs), this paper employs and compares three kinds of one-input neural network models, namely, multi-layer perceptron (MLP) networks, radial basis function (RBF) networks and wavelet neural networks (WNN) so as to select the best network model for further HRTFs approximation. Experimental results demonstrate that wavelet neural networks are more efficient and useful.     

采用人群搜索算法的径向基函数神经网络参数整定方法

针对径向基函数(RBF)神经网络的逼近结构中,对权值、基宽和中心向量的初始值等参数的选取不当,导致系统的鲁棒性变差、收敛精度降低,甚至不再收敛的问题,提出一种基于人群搜索算法的RBF神经网络的参数整定方法.以基于遗传算法和基于粒子群算法的RBF神经网络参数整定方法为对比条件,采用MATLAB软件进行实验与分析.结果表明:应用人群搜索算法去优化RBF神经网络的初始参数,能有效地提升RBF神经网络的逼近精度,验证了该算法的可行性.     

RBF网络自整定PID控制在网络化控制系统中的应用

文章针对网络化控制系统普遍存在的时延问题,介绍了一种基于径向基函数神经网络自整定PID的控制策略.在Matlab/Simulink环境下搭建了基于TrueTime工具箱的网络控制系统的仿真平台.仿真结果表明:与常规PID控制相比,神经网络自整定PID控制算法可有效地提高系统的鲁棒性和自适应性,且此方法易于实现,便于工程...     

Performance Comparison of Neural Networks for HRTFs Approximation

In order to approach to head-related transfer functions (HRTFs), this paper employs and compares three kinds of one-input neural network models, namely, multi-layer perceptron (MLP) networks, radial basis function (RBF) networks and wavelet neural networks (WNN) so as to select the best network model for further HRTFs approximation. Experimental results demonstrate that wavelet neural networks are more efficient and useful.     

支持向量机在机械设备振动信号趋势预测中的应用

将支持向量机（SVMs）用于机械设备振动信号趋势预测中，研究了SVMs参数及核函数类型对SVMs预测能力的影响．试验显示，在短期预测中4种核函数有着基本相同的预测能力，而在长期预测中，径向基函数核和多项式核表现出了相对较高的预测能力，同线性核和神经网络核相比，它们的归一化均方误差约降低了20％．SVMs与向后传播神经网络、径向基函数网络和广义回归神经网络预测能力的对比表明，实现了结构风险最小化原理的SVMs具有更好的预测能力，在长期预测中，其归一化均方误差约降低了15％。