Three Phase Fault Diagnosis Based on RBF Neural Network Optimized By PSO Algorithm

Modeling research on wheat protein content measurement using near-infrared reflectance spectroscopy and optimized radial basis function neural network

Neural Network system for SoC is one topic in the world. Recently, SoC began to combine AI technique by Python. When developers consider Intelligent system on SoC, the co-design software and hardware are significant. In this case, small devices and embedded system are target system. For IoT, those techniques will grow up in the world. Those cases, avoiding premature convergence while keeping performance is a challenge in training the neural network, especially in a case of large NNs or a large number of training data. In this case, one problem is a huge memory and processing power. For tiny memory and processing system as SoC, we propose an improved particle swarm optimization (PSO) algorithm called the PSOseed2 algorithm for training NN. The PSOseed2 algorithm solves the premature convergence of the standard PSO (SPSO) algorithm by slightly modifying the velocity update function without adding many computational tasks to the SPSO algorithm. We evaluated this algorithm on field programmable gate array (FPGA)-based NN and software-based NN and trained these NNs with different PSO algorithms that are SPSO, PSOseed, PSOseed2, and dissipative PSO. Experimental results with different datasets confirmed that the NNs trained by the proposed PSOseed2 algorithm had better recognition rates and lower global learning errors than the NN trained by other PSO algorithms. In this talk, I would like to talk those relational research in the world and our results and discussion.

Collaborative beamforming in wireless sensor networks using a novel particle swarm optimization algorithm variant

To propose a CT scanner tube fault diagnosis method based on a BP neural network optimized by particle swarm optimization (PSO) algorithm for identifying typical faults such as filament open circuit, filament half open circuit, scanning noise and arc discharge. Key parameters such as anode voltage, current, tube temperature, scanning time, and current fluctuation amplitude are selected, and the data set is constructed based on 357 sets of fault samples, which are divided into training set and test set. PSO is used to optimize the initial weights and thresholds of BP neural network, and PSO-BP model is constructed for fault classification. The classification accuracies of the model on the training and test sets are 96.25% and 92.31%, respectively, and the Pearson correlation coefficients of each fault type range from 0.894 to 0.971, indicating a strong linear relationship between the fault features and categories. Compared with the traditional methods, the PSO-BP model has obvious advantages in terms of accuracy and robustness. PSO-based BP neural network can effectively improve the diagnostic performance of typical faults of CT bulb tube, which has a good application prospect and provides a new method for the intelligent diagnosis of CT equipment.

There are many methods can be used to calculate ship's magnetic field such as harmonic wave analysis, finite element method, integral equation method, boundary element method and so on. But these methods generally have disadvantage of complex calculation and poor universality. In this paper, we have built a predicted model based on improved Radial Basis Function (RBF) neural network to study the relationship between ship's important parameters and magnetic field directly. Generally, the parameters of RBF are set by experience. In order to get the best result, we have chosen the spread parameter according to Particle Swarm Optimization (PSO) algorithm and verified the effect of network by an example.

Fault diagnosis for wireless sensor is very important to ensure signal acquisition precision. Radial basis function (RBF) neural network has strong classification ability. However, the selection of the connection weights, the hidden centers and the widths has an important influence on the classification performance of the RBF neural network in the learning process of RBF neural network. Thus, ant colony optimization is employ to gain the parameters of radial basis function neural network. Therefore, a novel method for fault diagnosis of wireless sensor based on RBF neural network and ant colony optimization is presented. The results of computational experiments show that ACO- RBF neural network has a great higher than RBF neural network.

The particle swarm optimization (PSO) algorithm presents a new way for finding optimal solutions of complex optimization problems. In this paper a modified particle swarm optimization algorithm is presented. We modify the PSO algorithm in some aspects. Firstly, a contractive factor is introduced to the position update equation, and the particles are limited in search region. A new strategy for updating velocity is then adopted, in which the velocity is weakened linearly. Thirdly, using an idea of intersecting two modified PSO algorithms. Finally, adding an item of integral control in the modified algorithm can improve its global search ability. Based on these strategies, we proposed a new PSO algorithm named crossed PSO algorithm. Simulation results show that the crossed PSO is superior to the original PSO algorithm and can get overall promising performance over a wide range of problems.

Efficient global optimization of aerodynamic shapes with the high-fidelity method is of great importance in the design process of modern aircrafts. In this study, modifications are made to the particle swarm optimization (PSO) algorithm and the radial basis function (RBF) model for further improvements of efficiency and accuracy of optimization. Specifically, a PSO algorithm with randomly distributed cognitive and social parameters, exponential decrease of maximum velocity and inertia weight, and periodic mutation of particle position is proposed. Furthermore, a nonuniform shape parameters strategy is introduced for the RBF surrogate model. Validations on test functions show that the new PSO has remarkable speed of convergence, and the new RBF model has superior approximation accuracy. The PSO algorithm and RBF model are then combined to construct the computational fluid dynamics (CFD)–based optimization framework. Finally, optimizations of transonic airfoil and supersonic launch vehicle are perfo...

For auto-body, fixture layout is very important to gravity deformation and dimensional variation of sheet metal assemblies. Conventionally, the satisfying fixture layouts can be generated by global optimization algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO) and social radiation algorithm (SRA), etc. However, it is very difficult to generate the global optimalsolution because the combinations of fixture layouts are enormousand the gravity deformation must be calculated by finite element analysis for sheet metal parts. Therefore, this paper proposes a hybrid algorithm of GAOT (genetic algorithm in optimization toolbox in MATLAB) and RBF-NN(Radial basis function - neural network) to generate the global optimal fixture layouts. In this paper, the fixture locators are selected from a much smaller candidate pool based on filtering methods. The objective function is the gravity deformation of all measurement operations. A hybrid algorithm of GAOT and RBF-NN is used to optimize fixture locating scheme based on MATLAB macro commands of 3DCS, whose optimization process is fully automated. A case of bracketis applied to demonstrate the proposed method in this paper. The results show that the proposedhybridalgorithm can generate better optimal solutions thanthe current optimization methods.

In order to solve the problem of interaction between multiple evaluation indexes of seed metering performance under multiple factors of centralized seed feeding device, a multi-objective optimization of structure based on particle swarm optimization (PSO) algorithm was proposed in this paper. The wheat centralized seed feeding device was taken as the research object, and the experimental factors were cone angle of type hole, working speed and seed filling gap. The working process of wheat centralized seed feeding device was simulated by discrete element method (DEM). The average seed number of type hole, the variation coefficient of the average seed number of type hole, and the maximum tangential force between seed and seed feeding mechanism were selected as the evaluation indexes. Through the variance analysis of the evaluation indexes by the experimental factors, the optimization objective function was constructed. Using PSO algorithm, the multi-objective optimization was carried out for the wheat centralized seed feeding device. The optimization results show that the best structural combination parameters of the wheat centralized seed feeding device are the hole cone angle of 31.6° and the seed filling gap of 4.6 mm. The validity of the method was verified by simulation and field test. The results show that the PSO algorithm multi-objective optimization method proposed in this paper can provide a reference for the structural improvement and optimal design of the centralized seed feeding device. Keywords: centralized seed feeding device, multi-objective, optimization, PSO algorithm DOI: 10.25165/j.ijabe.20201306.5665 Citation: Wang Q Q, Li Z D, Wang W W, Zhang C L, Chen L Q, Wan L. Multi-objective optimization design of wheat centralized seed feeding device based on particle swarm optimization (PSO) algorithm. Int J Agric & Biol Eng, 2020; 13(6): 76–84.

A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.

Networked synchronization control has very high technology level, which includes network technology and synchronization control technology, etc. Fault diagnosis of the devices in networked synchronization control system has a great importance for ensuring the normal operation. The radial basis function neural network with particle swarm optimization algorithm is developed. The combination method of RBF neural network and particle swarm optimization is applied to fault diagnosis of networked synchronization control system. The test results indicate that the combination model of RBF neural network and particle swarm optimization can almost entirely recognize each state of the device in networked synchronization control system. The diagnostic accuracy of the combination model of RBF neural network and particle swarm optimization is greater than that of normal RBF neural network.

The Particle swarm optimization (PSO) algorithm is a population-based intelligent stochastic search technique encouraged from the intrinsic manner of bee swarm seeking for their food source. With flexibility for numerical experimentation, the PSO algorithm has been mostly used to resolve diverse kind of optimization problems. The PSO algorithm is frequently captured in local optima meanwhile handling the complex real-world problems. Many authors improved the standard PSO algorithm with different mutation strategies but an exhausted comprehensive overview about mutation strategies is still lacking. This article aims to furnish a concise and comprehensive study of problems and challenges that prevent the performance of the PSO algorithm. It has tried to provide guidelines for the researchers who are active in the area of the PSO algorithm and its mutation strategies. The objective of this study is divided into two sections: primarily to display the improvement of the PSO algorithm with mutation strategies that may enhance the performance of the standard PSO algorithm to great extent and secondly, to motivate researchers and developers to use the PSO algorithm to solve the complex real-world problems. This study presents a comprehensive survey of the various PSO algorithms based on mutation strategies. It is anticipated that this survey would be helpful to study the PSO algorithm in detail for researchers.

The maximum power point tracking (MPPT) system ensures that the solar cell operates at its maximum power point, which is the point at which it produces the most power for a given set of conditions. This study uses an artificial bee colony (ABC) algorithm and particle swarm optimization (PSO) algorithms to optimize the boost converter. The ABC and PSO algorithms help to optimize the boost converter by finding the best possible duty cycle and other parameters that maximize efficiency. The resulting duty cycle of 20% produced the highest efficiency of 78.26% obtained from the boost converter. A system-wide test was conducted between 9:00 and 11:10 WIB. In MPPT systems using boost converters with the PSO algorithm, the average power is 16.80 W, while in MPPT systems using boost converters with the ABC algorithm, the average power is 14.53 W. The novelty of the study discusses a system designed to optimize power by using a boost converter in order to increase power output from solar panels. The algorithms PSO and ABC are used to analyze the data from the PV and adjust the converter parameters to optimize the output in order to increase the efficiency of the system.

In this paper, we present a concept of a transistor level implementation of the Particle Swarm Optimization (PSO) algorithm that belongs to the group of unsupervised learning algorithms aimed at the design of artificial neural networks (ANNs). The algorithm exhibits an ability to search for an optimal solution in a multidimensional data space, in which many sub-optimal solutions may exist. The ANN that operates in accordance with the PSO algorithm is composed of a set of cooperating particles (agents) that explore an input data space and communicate information on the best found solution to other particles. The PSO algorithm is usually implemented in software. We in our investigations focus on its transistor level realization. Such an approach enables parallel data processing, in which the overall data rate only moderately depends on the number of particles. Most of the operations and components of such implemented PSO algorithm may be reused considering our former CMOS realizations of other self-organizing learning algorithms. This allowed us to assess main parameters of the PSO.