基于GRNN算法的潜在产出与产出缺口估算模型

基于神经网络学习性、非参数估计的优势,建立估算经济潜在产出和产出缺口的广义回归神经网络（GRNN）模型,对中国产出缺口和潜在产出增速的1978—2015年数据进行估算、2016—2020年数据进行多步预测,实证分析较好地克服了生产函数法中由于事先假设确定的函数形式及选择不同函数结果不同的问题,并且所估算的潜在产出增速与经济增速具有较高的契合度。模型分析得出：中国经济增长速度大致维持在潜在产出增长率水平附近,便可实现通货膨胀率维持在合理水平,且较好地实现总体供需平衡的目的。     

Fuzzy Backpropagation Algorithms and Their Convergence

The equivalence between fuzzy neural networks model for max-min fuzzy operator and S.Stoeva's is proved by studying the fuzzy neural networks model for max-min fuzzy based on S.Stoeva's.Then the paper proposes the fuzzy backpropagation learning algorithms for changing fuzzy power and probes their convergence properties.Finally,it simulates experiment such as state monitoring of turbo-generator set.The results show that the fuzzy backpropagation learning algorithms presented are convergent on condition that the output of training sample is between maximum and minimum of its input.     

The application of rough sets-neural network theory to mine ventilation system evaluation

To solve the instability problem of established sample in the neural network evaluation method for mine ventilation system, a comprehensive evaluation of the ventilation system is carried out based on rough sets and BP neural networks. Taking the ventilation system of a mine as an example, the classification quality of raw data samples are tested by using rough set data analysis system. Then, based on artificial neural network theory, a rough sets-neural network evaluation model of a mine ventilation system is established and a new rough sets-neural network evaluation method of mine ventilation system is formed. The results show that, after the model validation of data and application, its theoretical evaluation results are in line with the actual situation, and the network total error is less than 0.004. It shows that the comprehensive evaluation method based on rough sets-neural networks has a good effect in evaluating mine ventilation system in practical application.     

我国南方经济开发区土地集约利用评价——以浙江、四川、贵州、海南为例

提高开发区土地集约利用评价结果的可靠性及可操作性,能合理地改善经营管理,挖掘土地利用潜力,不断提高开发区土地利用效率和经济效益,为开发区扩区升级提供参考依据和借鉴价值。本研究将我国南方地区按经济发达程度、交通便利及地理位置等方面分为三类不同地区,每类地区选择了两个经济开发区作为代表,采用层次分析法和改进模糊综合评价法耦合的方法,运用MATLAB7.0软件从横向角度对三类地区的经济开发区代表进行了评价。研究结果表明：区位和交通便捷,经济发达的浙江沿海地区土地集约利用水平较高,而地理位置比较特殊,经济欠发达的海南开发区的土地集约利用水平相对较低。     

A Kind of Fuzzy System Modelling and Control Based on Neural Networks

Combining artificial neural networks(ANN) with fuzzy system theory,a kind of modelling & control method of fuzzy system based on ANN is presented.The simulation researches have verified that the proposed approach can be applied effectively to a number of control systems which are defficult to build strict mathematical model.     

Design and Application of Compensatory Fuzzy Neural Network Based on Adaptive Learning Rate Method

To overcome the disadvantages of general networks such as slow convergence speed and being unable to combine with the expert knowledge etc,the authors introduces compensatory fuzzy neural cells,integrate the powerful knowledge expressiveness of fuzzy system and the excellent self-learning of neural network,and present a novel Compensatory Fuzzy Neural Network(CFNN) based on Adaptive Learning Rate Method which changes the learning rate using Adaptive Learning Rate Method in dynamic way.Finally this method is applied to the actual case.The result proves that it not(only) can adjust parameters properly on line,but also can optimize relevant fuzzy reasoning in dynamic way,fasten training rate.     

The Multi-objective Fuzzy Optimal Power Flow Model and its Neural Network Algorithm

This paper has presented a multi-objective fuzzy optimal power flow medel.Inthe model , the multiple objectives, such as the minimum generation cost and the minimum powerloss, have been considered simultaneously, A new algorithm based on neural network models is aisopresented,in which the neural networks are employed to express, the membership function of fuzzysets and solve the optimization problems. The validity of model and algorithm is verified with numerical examples.     

Using BP Neural Network to Determine the Membership Function of the Input of Fuzzy Control System

While design the fuzzy controller, it is very important to determine the membership function of fuzzy variables.The data can be broadly classified as fuzzy sets by using the classification property of the BP neural network. The author selects a BP neural network with one hide layer and uses S function to the input and hide layer, and linear function to the output layer.Advanced BP algorithm isused to train the BP neural network in the environment of MATLAB . The nearer to the target values is the better the last output is.With the trained BP network , the membership values of the inputs can be got ten. This method has high rate and low error.     

Dynamic Control Model for the Secondary Cooling of Slab Casting Based on Neural Networks

A dynamic control model for the secondary cooling of slab casting is presented to reduce the difference between the actual temperature and the goal surface temperature of slab. The model, which is based on the BP neural networks for forecasting the temperature and the fuzzy neural networks for dynamically controlling the water in the secondary cooling in the continuous casting, could timely adjust and allocate the water according to the speed and temperature of slab. A series of tests have been conducted based on inputs of the No. 2 slab caster in a steel plant. It has been shown that the model, which integrate the charateristics of water controlling problem in secondary cooling into the temperature status of slab during the cooling process, can control the water in secondary cooling efficiently and dynamically according to the situation of actual production.     

The Macroeconomic Alarming Model

This paper has used X city economic development as background,the economic situations of economic systems in different periods have been classified into five economic models by economic mathematic method,then,we have established the boundary cognition functions for each economic model,which provides the direct quantitative limit for the economic alarming.     

Evaluation of Academic Degrees and Graduate Education Based on Neural Network

With the development of the academic degrees and graduate education, evaluation is the important measure for guaranteeing the quality in the area. With increasing complication of the target of the evaluation of academic degrees and graduate education , it is necessary to introduce nonlinear method of the evaluation, which has great significance for the validity of the result of the evaluation. Neural networks have been developed greatly in recent years,they have the advantages of nonlinear mapping,parallelity ,adaption, etc. Therefore , they are good tools of nonlinear modeling. On the basis of setting up the model of the evaluation based on neural network ,this paper presets the practical value of the method.based them.     

The Application of Fuzzy Neural Networks for Power Transformer Fault Diagnosis

This paper presents the application of fuzzy neural networks (FNN) in power transformer faults diagnosis.A FNN model is builded,in which input is transformer oil color spectrum analysis and output is fault type.The test results of fault examples show its effectiveness and potential applicable worthiness.     

PID Control for Multivariable System Based on Ameliorative RBF Neural Networks

Aiming at systems which are of characteristics of multi-input and multi-output, nonlinearity and time-variation in the industrial control fields, this paper presents a intelligent PID control method based on ameliorative RBF neural networks, which constructs RBF neural networks identifier on-line and identifies a controlled object on-line by means of adopting the nearest neighbor-clustering algorithm, and adjusts parameters of PID controller on-line and realizes decoupiing control of multivariable, nonlinear and time-variation system. The simulation result indicates that the controller can get parameters which are optimal under some control law, it makes the decoupled system, compared to the PID control method based on the conventional RBF neural networks, has perfect dynamic and static performances, possesses the advantages of high precision, quick response speed and is of great adaptability and robustness.     

Study of Intelligent Fault Diagnostic System for a Kind of Power Plant

Based on the neural networks, object oriented technique, artificial intelligence technique and computer networks, an intelligent fault diagnosis system of the power plant whose need of the operation is presented. The architecture of software system , hardware system and corresponding databases is designed. The software of collecting data is established. The intelligent fault diagnosis system based on neural networks and object oriented technique is also developed. The physical system operation has been shown that the method mentioned above is feasible and effective in practice.     

The Fuzzy Inputing And Outputing Method in Vibration Fault Diagnosis of Steam Turbine-Generator Set

A fuzzy neural network diagnosis model is established on the basis of the vibration failure features of steam turbine-gernerator set, two kinds of fuzzy inputing method are discussed. At last, the performance of the fuzzy neural network is compared with that of the conventional BP network. The results show that the method presented is suitable for identifying the vibration failure of steam turbine-gernerator set, and it is more efficient in deal with the uncertain data than BP network diagnosis.     

Technique of Classification of Coal and Gas Outburst Mines

managing the coal mines according their dangerous grade determined. can decrease the engineering quanity and lessen the sightless characteristic. It has marked benefit for community and great economic benefit. The dangerous grade of outburst mines are determined Outburst coal mines are classified into mines with serious outburst hazard, mines with middle outburst hazard and mines with ordinary outburst hazard. Twelve classification indicators are used in comprehensively evaluating the dangerous grade of outburst mines. The dangerous grade of outburst mines are determined according to the principle of the most subjection with fuzzy comprehensive evaluation method, on the basis of the classification indicators and its quantification. By the model founded, some coal mines are studied and the research results are accordant with the fact.     

Predictive Control Approach for Active Power Filter Based on Neural Networks

In this paper, a predictive control approach using neural networks for active power filter is proposed. The control system of active power filter using this method make using of the internal model control technology based on neural networks, meanwhile, to solve its questions such as lag because of calculating using neural networks, a predictive model based on neural networks is introduced. Simulation analysis shows that this control approach can compensate the lag of system and take advantage of self-adaptive characteristic of neural networks. Good result can be obtained.     

Dynamic Modeling and Analysis for a Humanoid Robot

To resolve the kinematics modeling and dynamics modeling of a humanoid robot with complicated machine, a modeling method based on the conventional mechanism combined with neural network is presented. First the kinematics formulas are deduced by analyzing its geometry structure. Second, the dynamics models are built up with Lagrange-Euler method combined with the define of interactive force among all parts of the humanoid robot. The interactive force is realized by neural network method. Finally, the true robot parameters are applied to the model. The simulation results show the validity of the modeling. So, the method is practical and available.     

Driver Model of Steering Direction Control for Automobiles

Creating a suitable driver model is one of the most important parts in the closed-loop system of driver-vehicle-road.Several kinds of driver models have been created based on the traditional control theories such as transfer function,optimization control and the compensation control.But owing to the driver's characteristics of nonlinear and time-variable,it's difficult for those proposed driver models to mimic drivers' action.While accounting for the inherent manual control characteristics and limitations of the mankind,the intelligent control methods have been applied in creating driver models based on the fuzzy control,neural networks control and fuzzy-neural networks control theory.     

4个不同核桃品种物候观察及品质分析

为了给核桃优良性状选种提供理论依据,于2012年对‘西林3号’、‘香玲’、‘西扶1号’和‘契可’的物候期进行观察,并于9月采集果实后对这4个品种的单果重、坚果横径、坚果纵径、缝合线径、硬壳厚度和仁重进行变异系数、相关性和主成分分析。结果表明：只有‘契可’是雌先型核桃品种,‘西林3号’、‘香玲’、‘西扶1号’都为雄先型。通过对经济性状综合指标研究发现,‘香玲’品种的经济性状较其他3个品种更适合推广销售。以性状的累积方差贡献率达到80.971%确定了3个反映核桃主要经济性状的主成分,并通过计算品系的重要主成分值,对供试品系进行比较,进而选择综合经济性状优良的品系,其结果与品系的实际表现型相近似。表明,用主成分分析法对核桃主要经济性状综合评选较科学、简便。