网络优化法在兰溪南岸公园工程施工进度控制中的应用

该文总结网络优化法在兰溪南岸公园工程施工进度控制中的应用。通过采用网络优化法控制施工进度,工程最终提前工期45天。

风味蛋白酶水解合浦珠母贝肉制备抗菌肽人工神经网络法优化工艺

利用具有自学习特点的人工神经网络可实现对酶解过程的模拟仿真,研究从合浦珠母贝肉中制备抗菌肽的最佳工艺条件。采用3层(5-9-3)人工神经网络法对风味蛋白酶水解合浦珠母贝肉的工艺过程进行模拟和优化,并通过管碟抑菌法对产物的抑菌性质进行分析。结果表明:pH7.0、水解温度55℃、酶添加量1.6%、水解时间4h、料液比7:5,制备得到肽A,抑制鼠伤寒沙门氏菌最强,抑菌圈直径14.20mm,平均肽链长度2.6;pH7.0、水解温度55℃、酶添加量1.7%、水解时间4h、料液比3:2,制备得到肽B,抑制痢疾志贺氏菌最强,抑菌圈直径23.42mm,平均肽链长度2.8;pH6.5、水解温度60℃、酶添加量2.5%、水解时间4h、料液比7:5,制备得到肽C,对单核细胞增生李斯特菌的抑制效果最强,抑菌圈直径16.60mm,平均肽链长度2.5。3种抗菌肽对大肠杆菌、金黄色葡萄球菌也具有较强的抑菌效果,抑菌率为74.3%～80.8%。本研究利用人工神经网络优化制备的贝肉抗菌肽克服了纯度低、提取率低等缺点,为合浦珠母贝肉抗菌肽的开发利用提供技术支撑。

车身CAN总线网络数据传输效率优化算法的研究

针对CAN总线系统,定义了数据传输效率,分析了影响CAN网络数据传输效率的主要因素;通过计算比较,总结出3种可以有效提高CAN网络数据传输效率的方法,将它们结合起来,形成可有效利用CAN网络传输资源的CAN总线网络数据传输效率综合优化法。以典型车身CAN网络控制系统为例,运用该综合优化法进行了改进设计。结果表明利用该设计方法可以明显提高车身CAN网络的数据传输效率。

线性规划应用于工程网络图中时间—费用优化

利用线性规划模型对工程网络图中的时间—费用优化问题进行了研究 ,并与网络优化法进行了比较 ,得到了线性规划优于网络优化法的结果 .

矿井通风网路计算方法及应用

针对金川二矿区地面无主扇,井下采用多级机站通风的特点,系统地研究确定了仿真系统三要素,即通风系统、数字模型、计算机。角联风路自动识别和风网特征图自动绘制是该仿真系统所独有的特殊功能。系统采用的基于网络最小调节功耗概念的网络优化调节通路法既不同于线性规划法也不同于非线性规划法。在Auto CAD环境下实现了通风仿真系统的可视化。

浅谈系统规划理论在水电项目工期成本优化中的应用

在水利工程施工组织设计中，系统规划理论中的线性规划技术得到了广泛应用，文章把线性规划模型应用于解决水电工程施工中的工期成本优化问题，并应用计算机技术来解决工期成本优化的线性规划模型问题，为水电项目施工进度、成本管理提供了一条途径。

随机均匀网格优化法在橡胶配方优化中的应用研究

对随机均匀网格优化法求解橡胶配方优化问题的原理进行了较详细的分析，给出了优化变量域的一种收缩方法和计算收敛准则．丁基橡胶配方优化实例表明，该算法既有较快的收敛性， 又能以较大概率求得全局（一致收敛）极值点．

矿井通风仿真系统数学模型

系统地研究确定了仿真系统三要素,即通风系统、数字模型、计算机。与国内外类似成果相比较,角联风路自动识别、网络简化以及风网特征图自动绘制是该仿真系统所独有的特殊功能。系统采用的基于网络最小调节功耗概念的网络优化调节通路法,该法既不同于线性规划法也不同于非线性规划法。在Auto CAD环境下实现了通风仿真系统的可视化。

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.

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.

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.

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.

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.

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.