网络化方法在有机化学复习中的运用

应用网络原理，以结构、性质为主线，分析了烃与烃的衍生物的衍变关系。以各知识点为结点，设计各种类型实验，以利学生掌握重要有机物的特征反应和检验方法，联成有机物组成结构的知识网。

晶体管放大电路等效网络化简分析法

运用电源化简定理,给出分析晶体管放大电路的网络化简分析法,并结合分析具体电路说明此等效网络化简分析法的优势所在.

复杂网络短路电流的一种求解方法

在采用网络化简求解法求解复杂网络的短路电流时，需要人工进行大量复杂的数据运算和数据处理。本文通过求解线性方程组的克莱姆法则入手，结合工程实例详细介绍了运用Execl2000中的MDETREM函数求解复杂网络短路电流的计算方法，同时给出了该工程采用网络化简求解法求解短路电流的计算过程。结果表明，对于复杂网络的短路电流的求解，Execl2000中的MDETREM函数求解法比网络化简求解法具有明显的优越性。

Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm

A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.

Combining the genetic algorithms with artificial neural networks for optimization of board allocating

This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in board allocating of furniture production. In the experiment, the rectangular flake board of 3650 mm 1850 mm was used as raw material to allocate 100 sets of Table Bucked. The utilizing rate of the board reached 94.14 % and the calculating time was only 35 s. The experiment result proofed that the method by using the GA for optimizing the weights of the ANN can raise the utilizing rate of the board and can shorten the time of the design. At the same time, this method can simultaneously searched in many directions, thus greatly in-creasing the probability of finding a global optimum.

STRUCTURE OPTIMIZATION STRATEGY OF NORMALIZED RBF NETWORKS

Aimed at studying normali zed radial basis function network (NRBFN), this paper introduces the subtractiv e clustering based on a mountain function to construct the initial structure of NR BFN, adopts singular value decomposition (SVD) to analyze the relationship betwe en neural nodes of the hidden layer and singular values, cumulative contribution ratio, index vector, and optimizes the structure of NRBFN. Finally, simulation and performance comparison show that the algorithm is feasible and effective.

Springback prediction for incremental sheet forming based on FEM-PSONN technology

In the incremental sheet forming （ISF） process, springback is a very important factor that affects the quality of parts. Predicting and controlling springback accurately is essential for the design of the toolpath for ISF. A three-dimensional elasto-plastic finite element model （FEM） was developed to simulate the process and the simulated results were compared with those from the experiment. The springback angle was found to be in accordance with the experimental result, proving the FEM to be effective. A coupled artificial neural networks （ANN） and finite element method technique was developed to simulate and predict springback responses to changes in the processing parameters. A particle swarm optimization （PSO） algorithm was used to optimize the weights and thresholds of the neural network model. The neural network was trained using available FEM simulation data. The results showed that a more accurate prediction of s!oringback can be acquired using the FEM-PSONN model.

Distribution Network Reconfiguration Based on ESA

An efficient algorithm ESA combining evolution strategies(ES) with simulated annealing(SA) is proposed in this paper. We first use ES to choose an initial temperature, then use a modified SA to find a global optimum for the problem. An efficient load flow method and a heuristic criterion for determining the temperature lowering scheme are employed in order to speed up the computation. The solution algorithm has been tested on a distribution system with very promising results.

ANN Model and Learning Algorithm in Fault Diagnosis for FMS

The fault diagnosis model for FMS based on multi layer feedforward neural networks was discussed An improved BP algorithm,the tactic of initial value selection based on genetic algorithm and the method of network structure optimization were presented for training this model ANN(artificial neural network)fault diagnosis model for the robot in FMS was made by the new algorithm The result is superior to the rtaditional algorithm

Analysis of Mine Ventilation Network Using Genetic Algorithm

Aim To determine the global optimal solution for a mine ventilation network under given network topology and airway characteristics. Methods\ The genetic algorithm was used to find the global optimal solution of the network. Results\ A modified genetic algorithm is presented with its characteristics and principle. Instead of working on the conventional bit by bit operation, both the crossover and mutation operators are handled in real values by the proposed algorithms. To prevent the system from turning into a premature problem, the elitists from two groups of possible solutions are selected to reproduce the new populations. Conclusion\ The simulation results show that the method outperforms the conventional nonlinear programming approach whether from the viewpoint of the number of iterations required to find the optimum solutions or from the final solutions obtained.

Optimization of air quantity regulation in mine ventilation networks using the improved differential evolution algorithm and critical path method

In mine ventilation networks, the reasonable airflow distribution is very important for the production safety and economy. Three basic problems of the natural, full-controlled and semi-controlled splitting were reviewed in the paper. Aiming at the high difficulty semi-controlled splitting problem, the general nonlinear multi-objectives optimization mathematical model with constraints was established based on the theory of mine ventilation networks. A new algorithm, which combined the improved differential evaluation and the critical path method （CPM） based on the multivariable separate solution strategy, was put forward to search for the global optimal solution more efficiently. In each step of evolution, the feasible solutions of air quantity distribution are firstly produced by the improved differential evolu- tion algorithm, and then the optimal solutions of regulator pressure drop are obtained by the CPM. Through finite steps iterations, the optimal solution can be given. In this new algorithm, the population of feasible solutions were sorted and grouped for enhancing the global search ability and the individuals in general group were randomly initialized for keeping diversity. Meanwhile, the individual neighbor- hood in the fine group which may be closely to the optimal solutions were searched locally and slightly for achieving a balance between global searching and local searching, thus improving the convergence rate. The computer program was developed based on this method. Finally, the two ventilation networks with single-fan and multi-fans were solved. The results show that this algorithm has advantages of high effectiveness, fast convergence, good robustness and flexibility. This computer program could be used to solve lar^e-scale ~eneralized ventilation networks o^timization problem in the future.

An Effective Fault Diagnosis Method for Aero Engines Based on GSA-SAE

The health status of aero engines is very important to the flight safety.However,it is difficult for aero engines to make an effective fault diagnosis due to its complex structure and poor working environment.Therefore,an effective fault diagnosis method for aero engines based on the gravitational search algorithm and the stack autoencoder(GSA-SAE)is proposed,and the fault diagnosis technology of a turbofan engine is studied.Firstly,the data of 17 parameters,including total inlet air temperature,high-pressure rotor speed,low-pressure rotor speed,turbine pressure ratio,total inlet air temperature of high-pressure compressor and outlet air pressure of high-pressure compressor and so on,are preprocessed,and the fault diagnosis model architecture of SAE is constructed.In order to solve the problem that the best diagnosis effect cannot be obtained due to manually setting the number of neurons in each hidden layer of SAE network,a GSA optimization algorithm for the SAE network is proposed to find and obtain the optimal number of neurons in each hidden layer of SAE network.Furthermore,an optimal fault diagnosis model based on GSA-SAE is established for aero engines.Finally,the effectiveness of the optimal GSA-SAE fault diagnosis model is demonstrated using the practical data of aero engines.The results illustrate that the proposed fault diagnosis method effectively solves the problem of the poor fault diagnosis result because of manually setting the number of neurons in each hidden layer of SAE network,and has good fault diagnosis efficiency.The fault diagnosis accuracy of the GSA-SAE model reaches 98.222%,which is significantly higher than that of SAE,the general regression neural network(GRNN)and the back propagation(BP)network fault diagnosis models.

Global optimization by small-world optimization algorithm based on social relationship network

A fast global convergence algorithm, small-world optimization (SWO), was designed to solve the global optimization problems, which was inspired from small-world theory and six degrees of separation principle in sociology. Firstly, the solution space was organized into a small-world network model based on social relationship network. Secondly, a simple search strategy was adopted to navigate into this network in order to realize the optimization. In SWO, the two operators for searching the short-range contacts and long-range contacts in small-world network were corresponding to the exploitation and exploration, which have been revealed as the common features in many intelligent algorithms. The proposed algorithm was validated via popular benchmark functions and engineering problems. And also the impacts of parameters were studied. The simulation results indicate that because of the small-world theory, it is suitable for heuristic methods to search targets efficiently in this constructed small-world network model. It is not easy for each test mail to fall into a local trap by shifting into two mapping spaces in order to accelerate the convergence speed. Compared with some classical algorithms, SWO is inherited with optimal features and outstanding in convergence speed. Thus, the algorithm can be considered as a good alternative to solve global optimization problems.

An analytical method to calculate station evacuation capacity

The major objective of this work was to calculate evacuation capacity and solve the optimal routing problem in a given station topology from a network optimization perspective where station facilities were modelled as open finite queueing networks with a multi-objective set of performance measures. The optimal routing problem was determined so that the number of evacuation passengers was maximized while the service level was higher than a certain criterion. An analytical technique for modelling open finite queueing networks, called the iteration generalized expansion method(IGEM), was utilized to calculate the desired outputs. A differential evolution algorithm was presented for determining the optimal routes. As demonstrated, the design methodology which combines the optimization and analytical queueing network models provides a very effective procedure for simultaneously determining the service level and the maximum number of evacuation passengers in the best evacuation routes.

Automatic Change Detection for Road Networks from Images Based on GIS

Up to now, detailedstrategies and algorithms of automaticchange detection for road networksbased on GIS have not been discussed.This paper discusses two differentstrategies of automatic change detec-tion for images with low resolution andhigh resolution using old GIS data,and presents a buffer detection andtracing algorithm for detecting roadfrom low-resolution images and a newprofile tracing algorithm for detectingroad from high-resolution images. Forfeature-level change detection (FL-CD), a so-called buffer detection algo-rithm is proposed to detect changes offeatures. Some ideas and algorithms ofusing GIS prior information and somecontext information such as substructures of road in high-resolution imagesto assist road detection and extractionare described in detail.

A unified graphical method for integration of hydrogen networks with purification reuse

Introducing purifiers into hydrogen network can enhance the recovery and reuse of hydrogen in refineries, further reducing the consumption of fresh hydrogen. Based on previous graphical methods, this work proposes a simple and unified graphical method for integration of hydrogen networks with purification processes. Scenarios with different hydrogen concentrations of purified product can be analyzed by the unified procedure. As a result, the maximum hydrogen saved by purification reuse can he identified and the corresponding purification process can be optimized, The proposed method is easy and non-iterative, and it is valid to purification processes with any feed concentration. A conventional hydrogen network is analyzed to test the effectiveness of the proposed method.

Optimizing neural network forecast by immune algorithm

Considering multi-factor influence, a forecasting model was built. The structure of BP neural network was designed, and immune algorithm was applied to optimize its network structure and weight. After training the data of power demand from the year 1980 to 2005 in China, a nonlinear network model was obtained on the relationship between power demand and the factors which had impacts on it, and thus the above proposed method was verified. Meanwhile, the results were compared to those of neural network optimized by genetic algorithm. The results show that this method is superior to neural network optimized by genetic algorithm and is one of the effective ways of time series forecast.

A new artificial immune algorithm and its application for optimization problems

A new artificial immune algorithm （AIA） simulating the biological immune network system with selfadjustment function is proposed in this paper. AIA is based on the modified immune network model in which two methods of affinity measure evaluated are used, controlling the antibody diversity and the speed of convergence separately. The model proposed focuses on a systemic view of the immune system and takes into account cell-cell interactions denoted by antibody affinity. The antibody concentration defined in the immune network model is responsible directly for its activity in the immune system. The model introduces not only a term describing the network dynamics, but also proposes an independent term to simulate the dynamics of the antigen population. The antibodies＇ evolutionary processes are controlled in the algorithms by utilizing the basic properties of the immune network. Computational amount and effect is a pair of contradictions. In terms of this problem, the AIA regulating the parameters easily attains a compromise between them. At the same time, AIA can prevent premature convergence at the cost of a heavy computational amount （the iterative times）. Simulation illustrates that AIA is adapted to solve optimization problems, emphasizing muhimodal optimization.

User Association and Wireless Backhaul Bandwidth Allocation for 5G Heterogeneous Networks in the Millimeter-Wave Band

The user association and wireless backhaul bandwidth allocation for a two-tier heterogeneous network （HetNet） in the mil- limeter wave （mmWave） band is proposed in this article. The two-tier HetNet is built up with a macro base station （MBS） and several small cell SBSs, where the MBS is assumed to be equipped with large-scale antenna arrays but the SBSs only have single-antenna capa- bility and they rely on the wireless link to the MBS for backhaul. The sum of logarithmic user rate, which is established according to the result of multi-user Multiple Input Mul- tiple Output （MIMO） downlink employing Zero-Force Beamforming （ZFBF）, is chosen as the network utility for the objective func- tion. And a distributed optimization algorithm based on primal and dual decomposition is used to jointly optimize the user association variable xj,z and the wireless backhaul band- width factor α. Simulation results reveal that the distributed optimization algorithm jointly optimizing two variables outperforms the con- ventional SINR-based user association strate- gies.

Global Optimization for the Synthesis of Integrated Water Systems with Particle Swarm Optimization Algorithm

The problem of optimal synthesis of an integrated water system is addressed in this study, where water using processes and water treatment operations are combined into a single network such that the total cost of fresh water and wastewater treatment is globally minimized. A superstructure that incorporates all feasible design alterna- tives for wastewater treatment, reuse and recycle, is synthesized with a non-linear programming model. An evolutionary approach--an improved particle swarm optimization is proposed for optimizing such systems. Two simple examples are .Presented.to illustrate the global op.timization of inte.grated water networks using the proposed algorithm.