Hybrid optimization model of product concepts

Deficiencies of applying the simple genetic algorithm to generate concepts were specified. Based on analyzing conceptual design and the morphological matrix of an excavator, the hybrid optimization model of generating its concepts was proposed, viz. an improved adaptive genetic algorithm was applied to explore the excavator concepts in the searching space of conceptual design, and a neural network was used to evaluate the fitness of the population. The optimization of generating concepts was finished through the ＂evolution - evaluation＂ iteration. The results show that by using the hybrid optimization model, not only the fitness evaluation and constraint conditions are well processed, but also the search precision and convergence speed of the optimization process are greatly improved. An example is presented to demonstrate the advantages of the orooosed method and associated algorithms.     

Fuzzy neural and chaotic searching hybrid algorithm and its application in electric customers＇s credit risk evaluation

To evaluate the credit risk of customers in power market precisely, the new chaotic searching and fuzzy neural network （FNN） hybrid algorithm were proposed. By combining with the chaotic searching, the learning ability of the FNN was markedly enhanced. Customers＇ actual credit flaw data of power supply enterprises were collected to carry on the real evaluation, which can be treated as example for the model. The result shows that the proposed method surpasses the traditional statistical models in regard to the precision of forecasting and has a practical value. Compared with the results of ordinary FNN and ANN, the precision of the proposed algorithm can be enhanced by 2.2% and 4.5%, respectively.     

A random adaptive method to adjust MAC parameters in IEEE802.11e WLAN

The IEEE 802.11e standard is proposed to provide QoS support in WLAN by providing prioritized differentiation of traffic. Since all the stations in the same priority access category (AC) have the same set of parameters, when the number of stations increases, the probability of different stations in the same AC choosing the same values will increase, which will result in collisions. Random adaptive MAC (medium access control) parameters scheme (RAMPS) is proposed, which uses random adaptive MAC differentiation parameters instead of the static ones used in the 802.11e standard. The performance of RAMPS is compared with that of enhanced distributed coordination access (EDCA) using NS2. The results show that RAMPS can reduce collision rate of the AC and improve the throughput by using adaptive random contention window size and inter-frame spacing values. RAMPS ensures that at any given time, several flows of the same priority have different MAC parameter values. By using the random offset for the inter-frame spacing value and the backoff time, RAMPS can provide intra-AC differentiation. The simulation results show that RAMPS outperforms EDCA in terms of both throughput and end-to-end delay irrespective of the traffic load. Foundation item: Project(60673164) supported by the National Natural Science Foundation of China; Project(06JJ10009) supported by the Natural Science Foundation of Hunan Province, China; Project(20060533057) supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China; Project(2008CB317107) supported by the Major State Basic Research and Development Program of China; Project(NCET-05-0683) supported by the Program for New Century Excellent Talents in University     

Dynamic services selection algorithm in Web services composition supporting cross-enterprises collaboration

Based on the deficiency of time convergence and variability of Web services selection for services composition supporting cross-enterprises collaboration, an algorithm QCDSS (QoS constraints of dynamic Web services selection) to resolve dynamic Web services selection with QoS global optimal path, was proposed. The essence of the algorithm was that the problem of dynamic Web services selection with QoS global optimal path was transformed into a multi-objective services composition optimization problem with QoS constraints. The operations of the cross and mutation in genetic algorithm were brought into PSOA (particle swarm optimization algorithm), forming an improved algorithm (IPSOA) to solve the QoS global optimal problem. Theoretical analysis and experimental results indicate that the algorithm can better satisfy the time convergence requirement for Web services composition supporting cross-enterprises collaboration than the traditional algorithms. Foundation item: Project(70631004) supported by the Key Project of the National Natural Science Foundation of China; Project(20080440988) supported by the Postdoctoral Science Foundation of China; Project(09JJ4030) supported by the Natural Science Foundation of Hunan Province, China; Project supported by the Postdoctoral Science Foundation of Central South University, China     

Automated retinal blood vessels segmentation based on simplified PCNN and fast 2D-Otsu algorithm

According to the characteristics of dynamic firing in pulse coupled neural network (PCNN) and regional configuration in retinal blood vessel network, a new method combined with simplified PCNN and fast 2D-Otsu algorithm was proposed for automated retinal blood vessels segmentation. Firstly, 2D Gaussian matched filter was used to enhance the retinal images and simplified PCNN was employed to segment the blood vessels by firing neighborhood neurons. Then, fast 2D-Otsu algorithm was introduced to search the best segmentation results and iteration times with less computation time. Finally, the whole vessel network was obtained via analyzing the regional connectivity. Experiments implemented on the public Hoover database indicate that this new method gets a 0.803 5 true positive rate and a 0.028 0 false positive rate on an average. According to the test results, compared with Hoover algorithm and method of PCNN and 1D-Otsu, the proposed method shows much better performance. Foundation item: Project (60872081) supported by the National Natural Science Foundation of China; Project (50051) supported by the Program for New Century Excellent Talents in University; Project (4092034) supported by the Natural Science Foundation of Beijing     

General scheduling framework in computational Grid based on Petri net

A general scheduling framework (GSF) for independent tasks in computational Grid is proposed in this paper, which modeled by Petri net and located on the layer of Grid scheduler. Furthermore, a new mapping algorithm aimed at time and cost is designed on the basis of this framework. The algorithm uses weighted average fuzzy applicability to express the matching degree between available machines and independent tasks. Some existent heuristic algorithms are tested in GSF, and the results of simulation and comparison not only show good flexibility and adaptability of GSF, but also prove that, given a certain aim, the new algorithm can consider the factors of time and cost as a whole and its performance is higher than those mentioned algorithms. Foundation item: Project (60433020) supported by the National Natural Science Foundation of China; project supported by the Postdoctoral Science Foundation of Central South University     

Improved PSO algorithm and its application

The mechanism of particle swarm optimization algorithm is studied, and one can draw the conclusion that the best particle found by the swarm falling into local minima is one of the main reasons for premature convergence. Therefore, an improved particle swarm optimization algorithm is proposed. This algorithm selects the best particle with roulette wheel selection method, so premature converging to local optima is avoided. At last, the improved particle swarm optimization algorithm is applied to optimization of time-sharing power supply for zinc electrolytic process. Simulation and practical results show that the global search ability of IPSO is improved greatly and optimization of time-sharing power supply for zinc electrolytic process can bring about outstanding economic benefit for plant. Foundation item: Project (2002CB312200) supported by the National Key Research 973 Program of China     

An efficient embedding tree matching algorithm based on metaphoric dependency syntax tree

To find out all dependency relationships in which metaphors probably exist between syntax constituents in a given sentence, a dependency tree matching algorithm oriented to Chinese metaphor processing is proposed based on a research of unordered tree inclusion matching. In this algorithm, the pattern library is composed of formalization dependency syntax trees that are derived from large-scale metaphor sentences. These kinds of metaphor sentences are saved in the pattern library in advance. The main process of this algorithm is up-down searching and bottom-up backtracking revising. The algorithm discovers potential metaphoric structures in Chinese sentences from metaphoric dependency pattern library. Finally, the feasibility and efficiency of the new matching algorithm are further testified by the results of a series of experiments on dependency pattern library. Hence, accurate dependency relationships can be achieved through this algorithm. Foundation item: Project(50474033) supported by the National Natural Science Foundation of China     

A bi-objective model for job-shop scheduling problem to minimize both energy consumption and makespan

The issue of reducing energy consumption for the job-shop scheduling problem in machining systems is addressed, whose dual objectives are to minimize both the energy consumption and the makespan. First, the biobjective model for the job-shop scheduling problem is proposed. The objective function value of the model represents synthesized optimization of energy consumption and makespan. Then, a heuristic algorithm is developed to locate the optimal or near optimal solutions of the model based on the Tabu search mechanism. Finally, the experimental case is presented to demonstrate the effectiveness of the proposed model and the algorithm. Foundation item: Project (50475062) supported by the National Natural Science Foundation of China; project (2004-47-19) supported by the Natural Science Foundation of Chongqing City of China     

一种前馈神经网络基于U－D分解渐消记忆滤波的学习方法

针对前馈网络ＢＰ算法所存在的收敛速度慢且常遇局部极小值等缺陷，提出一种基于Ｕ－Ｄ分解的渐消记忆推广卡尔曼滤波学习新方法，与ＥＫＦ相比，不仅大大加快了学习收敛速度、数值稳定性好，而且比ＢＰ算法需较少的学习次数和隐节点数仍可达到更好的学习效果。仿真计算表明，该方法是提高网络学习速度、改善学习效果的一种有效方法，可有效解决非线性系统建模、辨识与控制问题。     

Typical structures for learning control

Some typical structural schemes of learning control have been investigated. The schemes involve the pattern recognition-based learning control, iterative learning control, repetitive learning control, and connectionist learning control, etc. This study focuses on the control mechanism and provides a basis for potential applications. Most of the structural schemes have been applied to various control fields. Project supported by the National Natural Science Foundation of China, the National Doctorate Program Fund of China, and the R&D Fund of the Nonferrous Metals Corporation of China Synopsis of the author Cai Zixing, professor, born in 1938. Research areas include intelligent control and intelligent automation, robot planning and control, industrial process control and automation.     

A sink moving scheme based on local residual energy of nodes in wireless sensor networks

In the application of periodic data-gathering in sensor networks, sensor nodes located near the sink have to forward the data received from all other nodes to the sink, which depletes their energy very quickly. A moving scheme for the sink based on local residual energy was proposed. In the scheme, the sink periodically moves to a new location with the highest stay-value defined by the average residual energy and the number of neighbors. The scheme can balance energy consumption and prevent nodes around sink from draining their energy very quickly in the networks. The simulation results show that the scheme can prolong the network lifetime by 26%–65% compared with the earlier schemes where the sink is static or moves randomly. Foundation item: Project(60673164) supported by the National Natural Science Foundation of China; Project(20060533057) supported by the Specialized Research Foundation for the Doctoral Program of Higher Education of China     

Bi-level programming model for reconstruction of urban branch road network

Considering the decision-making variables of the capacities of branch roads and the optimization targets of lowering the saturation of arterial roads and the reconstruction expense of branch roads, the bi-level programming model for reconstructing the branch roads was set up. The upper level model was for determining the enlarged capacities of the branch roads, and the lower level model was for calculating the flows of road sections via the user equilibrium traffic assignment method. The genetic algorithm for solving the bi-level model was designed to obtain the reconstruction capacities of the branch roads. The results show that by the bi-level model and its algorithm, the optimum scheme of urban branch roads reconstruction can be gained, which reduces the saturation of arterial roads apparently, and alleviates traffic congestion. In the data analysis the arterial saturation decreases from 1.100 to 0.996, which verifies the micro-circulation transportation’s function of urban branch road network. Foundation item: Project(2006CB705507) supported by the National Basic Research and Development Program of China; Project(20060533036) supported by the Specialized Research Foundation for the Doctoral Program of Higher Education of China     

时延Elman递归神经网络及其在PMSM的混沌控制中的应用

针对Elman递归神经网络存在的高深度、低分辨率问题,提出了一个结构简单的时延Elman递归神经网络模型。通过在Elman递归神经网络中引入多步的时延结构和反馈结构增强网络的记忆深度和分辨率。针对永磁同步电动机(PMSM)中存在的混沌运动,设计了时延Elman递归神经网络控制器和辨识器,推导出时延Elman递归神经网络的动态反传算法。运用离散型Lyapunov稳定判据,推导出此神经网络控制器和辨识器的权值自适应学习速率的取值范围,确保了控制系统的稳定性和快速收敛性。仿真结果表明,作者提出的时延Elman递归神经网络在动态系统的辨识和控制等方面具有良好的性能。     

Short-term forecasting optimization algorithms for wind speed along Qinghai-Tibet railway based on different intelligent modeling theories

To protect trains against strong cross-wind along Qinghai-Tibet railway, a strong wind speed monitoring and warning system was developed. And to obtain high-precision wind speed short-term forecasting values for the system to make more accurate scheduling decision, two optimization algorithms were proposed. Using them to make calculative examples for actual wind speed time series from the 18th meteorological station, the results show that: the optimization algorithm based on wavelet analysis method and improved time series analysis method can attain high-precision multi-step forecasting values, the mean relative errors of one-step, three-step, five-step and ten-step forecasting are only 0.30%, 0.75%, 1.15% and 1.65%, respectively. The optimization algorithm based on wavelet analysis method and Kalman time series analysis method can obtain high-precision one-step forecasting values, the mean relative error of one-step forecasting is reduced by 61.67% to 0.115%. The two optimization algorithms both maintain the modeling simple character, and can attain prediction explicit equations after modeling calculation. Foundation item: Project(2006BAC07B03) supported by the National Key Technology R & D Program of China; Project(2006G040-A) supported by the Foundation of the Science and Technology Section of Ministry of Railway: Project(2008yb044) supported by the Foundation of Excellent Doctoral Dissertation of Central South University     

The Bonding Properties of Solid Steel to Liquid Aluminum

The bonding of solid steel plate to liquid aluminum was studied using rapid solidification. The relationship models of interfacial shear strength and thickness of interfacial layer of bonding plate vs bonding parameters ( such as preheat temperature of steel plate, temperature of aluminum liquid and bonding time ) were respectively established by artificial neural networks perfectly. The bonding parameters for the largest interfacial shear strength were optimized with genetic algorithm successfully. They are 226℃ for preheating temperature of steel plate, 723℃ for temperature of aluminum liquid and 15.8s for bonding time, and the largest interfacial shear strength of bonding plate is 71.6MPa. Under these conditions, the corresponding reasonable thickness of interfacial layer (10.8μm) is gotten using the relationship model established by artificial neural networks.     

Rapid prediction method for nonlinear expansion process of medical vascular stent

A neural network model with high nonlinear recognition capability was constructed to describe the relationship between the deformation impact factors and the deformation results of vascular stent.Then,using the weighted correction method with the attached momentum term,the network training algorithm was optimized by introducing learning factor η and momentum factor ψ,so the speed of the network training and the system robustness were enhanced.The network was trained by some practi-cal cases,and the statisti...     

Four optimal design approaches of high-order finite-impulse response filters based on neural network

Four optimal approaches of high-order finite-impulse response(FIR) digital filters were developed for designing four types filters using neural network algorithms. The solutions were presented as parallel algorithms to approximate the desired frequency response specification. Therefore, these methods avoid matrix inversion, and make a fast calculation of the filter’s coefficients possible. The convergence theorems of these proposed algorithms were presented and proved to illustrate them stable, and the implementation of these methods was described together with some design guidelines. The simulation results show that the ripples of the designed FIR filters are significantly little in the pass-band and stop-band, and the proposed algorithms are of fast convergence.     

无模型动态摩擦的自回归小波神经补偿控制

针对低速伺服系统的摩擦补偿问题,提出一种基于自回归小波神经网络的智能控制算法,无需预知系统的动力模型参数,仅通过闭环位置反馈,网络即能利用极少的神经元和迭代次数实现对非线性摩擦的高精度补偿.Lya-punov稳定性分析结果证明了跟踪误差和网络权值的有界收敛性.某型机器人关节的伺服实验结果表明,引入自回归小波神经补偿算法后的伺服定位精度得以大幅度提高.