Particle Swarm Optimization Back Propagation Research Articles

Reconstruction and prediction of capillary pressure curve based on Particle Swarm Optimization-Back Propagation Neural Network method

Abstract Capillary pressure curve plays a critical role in the reservoir evaluation. It is essential to reconstruct and predict capillary pressure curve properly. Many traditional capillary pressure correlations have been suggested in the literature. However, their major limitation is mainly applicable to homogenous reservoir, and the larger error will be caused when heterogeneous reservoir is dealt by using these mathematical correlations. This study aims at providing an important method based on Particle Swarm Optimization-Back Propagation Neural Network (PSO-BP neural network) to represent and predict capillary pressure curve for homogenous and heterogeneous reservoir. The combination of PSO algorithm and BP neural network converges quickly, which improves the accuracy and efficiency of simulation. In this paper, core samples from three blocks of the same marine-sand reservoir, whose porosity is between 0.6% and 20.0% and permeability is between 0.1mD and 6117mD, are investigated by PSO-BP neural network method and J-Function method respectively. The reconstruction and prediction results are compared with the results obtained by mercury intrusion method in laboratory. The results show that capillary pressure curves reconstructed and predicted by PSO-BP neural network method are in better agreement with mercury intrusion curves than J-Function method, with 0.1%–5% and 5%–8% relative error respectively, which can totally meet the in-situ requirements. It is also demonstrated that PSO-BP neural network method is more suitable for homogenous and heterogeneous reservoir.

A Big Data based Cost Prediction Method for Remanufacturing End-of-Life Products

Remanufacturing is considered as an important industrial process to restore the performance and function of End-of-Life (EOL) products to a like-new state. In order to help enterprises effectively and precisely predict the cost of remanufacturing processes, a remanufacturing cost prediction model based on big data is developed. In this paper, a cost analysis framework is established by applying big data technologies to interpret the obtained data, identify the intricate relationship of obtained sensor data and its corresponding remanufacturing processes and associated costs. Then big data mining and particle swarm optimization Back Propagation (BP) neural network algorithm are utilized to implement the cost prediction. The application of presented model is verified by a case study, and the results demonstrates that the developed model can predict the cost of the remanufacturing accurately allowing early decision making for remanufacturability of the EOL products.

A Precise Positioning Method for a Puncture Robot Based on a PSO-Optimized BP Neural Network Algorithm

The problem of inverse kinematics is fundamental in robot control. Many traditional inverse kinematics solutions, such as geometry, iteration, and algebraic methods, are inadequate in high-speed solutions and accurate positioning. In recent years, the problem of robot inverse kinematics based on neural networks has received extensive attention, but its precision control is convenient and needs to be improved. This paper studies a particle swarm optimization (PSO) back propagation (BP) neural network algorithm to solve the inverse kinematics problem of a UR3 robot based on six degrees of freedom, overcoming some disadvantages of BP neural networks. The BP neural network improves the convergence precision, convergence speed, and generalization ability. The results show that the position error is solved by the research method with respect to the UR3 robot inverse kinematics with the joint angle less than 0.1 degrees and the output end tool less than 0.1 mm, achieving the required positioning for medical puncture surgery, which demands precise positioning of the robot to less than 1 mm. Aiming at the precise application of the puncturing robot, the preliminary experiment has been conducted and the preliminary results have been obtained, which lays the foundation for the popularization of the robot in the medical field.

Seismic liquefaction potential assessed by neural networks

This study presents two optimization techniques: genetic algorithm (GA) and particle swarm optimization (PSO), to improve the efficiency of backpropagation (BP) neural network model for predicting liquefaction susceptibility of soil. A detailed parametric study is designed and performed to find the optimal parameters of GA and PSO, respectively. The database used in this study includes 166 CPT-based field observations from more than eight major earthquakes between 1964 and 1983. Six factors including cone resistance, total vertical stress, effective vertical stress, depth of penetration, normalized peak horizontal acceleration at ground surface and earthquake magnitude are selected as the evaluating indices. The predictions from the PSO–BP model were compared with those from two models: BP and GA–BP. The study concluded that the proposed PSO–BP model improves the classification accuracy and is a feasible method in predicting soil liquefaction.

A Heuristic Diagnostic Method for a PV System: Triple-Layered Particle Swarm Optimization–Back-Propagation Neural Network

This paper proposes a heuristic triple layered particle swarm optimization–back-propagation (PSO-BP) neural network method for improving the convergence and prediction accuracy of the fault diagnosis system of the photovoltaic (PV) array. The parameters, open-circuit voltage (Voc), short-circuit current (Isc), maximum power (Pm) and voltage at maximum power point (Vm) are extracted from the output curve of the PV array as identification parameters for the fault diagnosis system. This study compares performances of two methods, the back-propagation neural network method, which is widely used, and the heuristic method with MATLAB. In the training phase, the back-propagation method takes about 425 steps to convergence, while the heuristic method needs only 312 steps. In the fault diagnosis phase, the prediction accuracy of the heuristic method is 93.33%, while the back-propagation method scores 86.67%. It is concluded that the heuristic method can not only improve the convergence of the simulation but also significantly improve the prediction accuracy of the fault diagnosis system.

Rock strength estimation: a PSO-based BP approach

Application of back-propagation (BP) artificial neural network (ANN) as an accurate, practical and quick tool in indirect estimation of uniaxial compressive strength (UCS) of rocks has recently been highlighted in the literature. This is mainly due to difficulty in direct determination of UCS in laboratory as preparing the core samples for this test is troublesome and time-consuming. However, ANN technique has some limitations such as getting trapped in local minima. These limitations can be minimized by combining the ANNs with robust optimization algorithms like particle swarm optimization (PSO). This paper gives insight into development of a hybrid PSO–BP predictive model of UCS. For this reason, dataset comprising the results of 228 laboratory tests including dry density, moisture content, P wave velocity, point load index test, slake durability index and UCS was prepared. These tests were conducted on 38 sandstone samples which were taken from two excavation sites in Malaysia. Findings showed that PSO–BP model performs well in predicting UCS. Nevertheless, to compare the prediction performance of the PSO–BP model, the UCS is predicted using ANN-based PSO and BP models. The correlation coefficient, R, values equal to 0.988 and 0.999 for training and testing datasets, respectively, suggest that the PSO–BP model outperforms the other predictive models.

Intelligent Nadaboost-ELM Modeling Method for Formation Drillability Using Well Logging Data

Drillability is a precondition for drilling-trajectory planning and intelligent control, as well as an important foundation for achieving safety, high quality, and efficiency in deep drilling. A formation drillability modeling method based on the Nadaboost-ELM algorithm is proposed. First, well logging parameters are chosen as inputs of the extreme learning machine (ELM) model, whose output is the formation drillability. Then, the models are trained as weak learners using the improved Adaboost algorithm. Finally, the weak learners are combined into a strong learner. The proposed modeling method is used for regression and prediction. In the regression aspect, the comparison results indicate that the proposed method has higher accuracy than methods in other studies, e.g., multiple regression (MR), grey model (GM), particle-swarm optimization back-propagation (PSO-BP), etc. In the prediction aspect, the results show that the proposed method is better than other prediction methods, e.g., MR, GM, BP, PSO-BP, ELM, and Adaboost-ELM at improving the model’s prediction accuracy, which provides a foundation for intelligent geological drilling.

Design Optimization and Fabrication of High-Sensitivity SOI Pressure Sensors with High Signal-to-Noise Ratios Based on Silicon Nanowire Piezoresistors.

In order to meet the requirement of high sensitivity and signal-to-noise ratios (SNR), this study develops and optimizes a piezoresistive pressure sensor by using double silicon nanowire (SiNW) as the piezoresistive sensing element. First of all, ANSYS finite element method and voltage noise models are adopted to optimize the sensor size and the sensor output (such as sensitivity, voltage noise and SNR). As a result, the sensor of the released double SiNW has 1.2 times more sensitivity than that of single SiNW sensor, which is consistent with the experimental result. Our result also displays that both the sensitivity and SNR are closely related to the geometry parameters of SiNW and its doping concentration. To achieve high performance, a p-type implantation of 5 × 1018 cm−3 and geometry of 10 µm long SiNW piezoresistor of 1400 nm × 100 nm cross area and 6 µm thick diaphragm of 200 µm × 200 µm are required. Then, the proposed SiNW pressure sensor is fabricated by using the standard complementary metal-oxide-semiconductor (CMOS) lithography process as well as wet-etch release process. This SiNW pressure sensor produces a change in the voltage output when the external pressure is applied. The involved experimental results show that the pressure sensor has a high sensitivity of 495 mV/V·MPa in the range of 0–100 kPa. Nevertheless, the performance of the pressure sensor is influenced by the temperature drift. Finally, for the sake of obtaining accurate and complete information over wide temperature and pressure ranges, the data fusion technique is proposed based on the back-propagation (BP) neural network, which is improved by the particle swarm optimization (PSO) algorithm. The particle swarm optimization–back-propagation (PSO–BP) model is implemented in hardware using a 32-bit STMicroelectronics (STM32) microcontroller. The results of calibration and test experiments clearly prove that the PSO–BP neural network can be effectively applied to minimize sensor errors derived from temperature drift.

A Novel Hybrid Localization Method for Wireless Sensor Network

Abstract Wireless sensor network is a kind of brand-new information acquisition platform, which is realized by the introduction of self-organizing and auto-configuration mechanisms. Node localization technology represents a crucial component of wireless sensor network. In this paper, a localization method based on kernel principal component analysis and particle swarm optimization back propagation algorithm is carefully discussed. First of all, taking KPCA as the front-end system to extract the main components of the localization information, and then regarding the nonlinear principal components extracted from distance vectors as the input samples, and meanwhile taking the coordinates of vertices in addition to the region boundary as the output samples, the PSO-BP neural network is trained to achieve the localization model. Finally the localization of unknown nodes can be estimated. The simulation experiment result showed that the method has high ability of stability and precision, and meets the practical need of localization.

Performance enhancement of INS/CNS integration navigation system based on particle swarm optimization back propagation neural network

For the surface ship integrated navigation system of INS/CNS, the Star Sensor may invalidity due to the cloud weather, which leads to the integrated system cannot work anymore. To resolve the problem, an INS/CNS integrated navigation method based on particle swarm optimization back propagation neural network (PSO BPNN) is proposed in this paper. During the effective Star Sensor navigation process, the INS positioning error can be obtained and used for training PSO BPNN; when the Star Sensor is in invalid state, the already trained BPNN is used for forecasting the INS positioning error. The effectiveness of this approach was demonstrates by simulation and experimental study. The results showed that the INS/CNS integrated navigation method based on PSO BPNN can effectively estimate and compensate the INS navigation error under the star senor invalid state.

Learning an Artificial Neural Network Using Dynamic Particle Swarm Optimization-Backpropagation: Empirical Evaluation and Comparison

Training neural networks is a complex task with great importance in the field of supervised learning. In the training process, a set of input?output patterns is repeated to an artificial neural network (ANN). From those patterns weights of all the interconnections between neurons are adjusted until the specified input yields the desired output. In this paper, a new hybrid algorithm is proposed for global optimization of connection weights in an ANN. Dynamic swarms are shown to converge rapidly during the initial stages of a global search, but around the global optimum, the search process becomes very slow. In contrast, the gradient descent method can achieve faster convergence speed around the global optimum, and at the same time, the convergence accuracy can be relatively high. Therefore, the proposed hybrid algorithm combines the dynamic particle swarm optimization (DPSO) algorithm with the backpropagation (BP) algorithm, also referred to as the DPSO-BP algorithm, to train the weights of an ANN. In this paper, we intend to show the superiority (time performance and quality of solution) of the proposed hybrid algorithm (DPSO-BP) over other more standard algorithms in neural network training. The algorithms are compared using two different datasets, and the results are simulated.

Using a back propagation neural network based on improved particle swarm optimization to study the influential factors of carbon dioxide emissions in Hebei Province, China

The emission of carbon dioxide is the primary cause of the greenhouse effect, therefore a precise study of the influential factors of carbon dioxide emissions is of great significance to control the growth from the source. In this paper, non-inertial weight coefficients and selective mutation strategies are used in a particle swarm optimization algorithm, and the improved particle swarm was used to optimize the initial connection weights and thresholds of a traditional back propagation (BP) neural network. Consequently, a new BP model based on an improved particle swarm (IPSO) is established: improved particle swarm optimization-back propagation algorithm (IPSO-BP). In order to verify the overall performance and effectiveness of the proposed method, an empirical analysis of carbon dioxide emissions and influential factors was carried out in Hebei Province, China during the period 1978–2012. The results were compared with those of two other methods to prove that the proposed IPSO-BP algorithm could take full advantage of IPSO's global search capability and BP's local search capability, as well as overcome the problems of BP of random initial values and premature solutions. In addition, the precision of the fit and prediction of carbon dioxide emissions are improved notably.

Tool life prediction based on particle swarm optimization–back-propagation neural network

As one of the most important factors affecting shop floor management, tool life is determined by the tool flank wear or break, which is related to the tool parameters, cutting conditions and workpiece parameters. It is found that the relationship between these factors and the tool life is too nonlinear to be analytically formulated. For this reason, back-propagation neural network model is used to predict the tool life for its strong ability of nonlinear fitness. To avoid the local optimum, slow convergence and low generalization capability of the back-propagation neural network, a tool life prediction model, which is based on improved particle swarm optimization–back-propagation neural networks, is proposed in this article. The particle swarm optimization is applied to optimize the weights and thresholds of the back-propagation algorithm for improving the ability of global search and generality. Existing sample data of tools are used to train the proposed model for predicting the life of the tools that are similar to the sample data in tool style. The face milling tools and workpiece of 45 steel are selected for experiment. Theoretical analysis and comparative experiments with back-propagation neural network indicate that the life predicted by the particle swarm optimization–back-propagation model is much better than that of the back-propagation model. It proves that particle swarm optimization–back-propagation model has better convergence, stronger robustness and higher generality. This model also provides a theoretical basis for the economization of tool demand analysis and production planning.

Identification and Modeling of a Yeast Fermentation Bioreactor Using Hybrid Particle Swarm Optimization-Artificial Neural Networks

In recent years, artificial neural networks with back-propagation training have been widely used in chemical and petroleum engineering. In this article, particle swarm optimization was combined with a back propagation algorithm to form a new learning algorithm for training artificial neural networks. The particle swarm optimization algorithm was shown to converge rapidly during the initial stages of a global search, but around global optimum, the search process will become very slow. On the contrary, the gradient descending method can achieve a faster convergent speed around global optimum, and at the same time, the convergent accuracy can be higher. The proposed algorithm combines the local searching ability of the gradient-based back-propagation strategy with the global searching ability of particle swarm optimization. Particle swarm optimization is used to decide the initial weights of the gradient decent methods. This strategy is applied to model a highly nonlinear system of yeast fermentation bioreactor. Based on the results comparison, the particle swarm optimization-back propagation model is found to be superior to the back propagation-artificial neural network model in identification of nonlinear systems.

Hybrid Swarm Algorithm for the Suppression of Incubator Interference in Premature Infants ECG

The premature infant Electrocardiography (ECG) is always contaminated by an electromagnetic interference caused by the incubator devices. This study describes the interference noise cancelling techniques for filtering of the corrupted infant ECG signal using the biological inspired Particle Swarm Optimization (PSO) algorithm. The active noise control system is designed using a adaptive learning ability of artificial neural network Back propagation algorithm. The neural weights are adapted based in PSO in an adaptive manner. In this study, the hybrid Particle Swarm Optimization-Back Propagation Neural Network (PSO-BPNN) feed forward algorithm is used for the Active Noise Control (ANC) of the fundamental electromagnetic interference in the incubators. The results showed the incubator noise can be significantly reduced using the developed hybrid PSO-BPNN algorithm. To implement this process of noise cancellation, the software used is MATLAB 7.10 with the help of neural network toolbox.

Permeability Estimation of the Reservoir Based on Particle Swarm Optimization Coupled with Artificial Neural Networks

In this work we investigate how the integration of back-propagation (BP) with particle swarm optimization (PSO) improves the reliability and prediction capability of PSO. This strategy is applied to predict permeability in Mansuri Bangestan reservoir located in Ahwaz, Iran, utilizing available geophysical well log data. Our methodology utilizes a hybrid PSO–BP. The particle swarm optimization algorithm was shown to converge rapidly during the initial stages of a global search, but around global optimum, the search process will become very slow. On the contrary, the gradient descending method can achieve faster convergence speed around global optimum and with greater accuracy. The proposed algorithm combines the local search ability of the gradient-based BP strategy with the global search ability of particle swarm optimization. PSO is used to decide the initial weights of the gradient decent methods so that all of the initial weights can be searched intelligently. The experimental results show that the proposed hybrid PSO–BP algorithm is better than the PSO algorithm in convergence speed and accuracy.

CHORD RECOGNITION USING NEURAL NETWORKS BASED ON PARTICLE SWARM OPTIMIZATION

A sequence of musical chords can facilitate musicians in music arrangement and accompaniment. To implement an intelligent system for chord recognition, in this article we propose a novel approach using artificial neural networks (ANN) trained bythe particle swarm optimization (PSO) technique and back-propagation (BP) learning algorithm. All of the training and testing data are generated from musical instrument digital interface (MIDI) symbolic data. Furthermore, in order to improve the recognition efficiency, an additional feature of cadencesis included. In other words, cadence is not only the structural punctuation of a melodic phrase but is considered as the important feature for chord recognition. Experimental results of our proposed approach show that adding a cadence feature significantly improves recognition rate, and the ANN-PSO method outperforms ANN-BP in chord recognition. In addition, because preliminary experimental recognition rates are generally not stable enough, we chose the optimal ANNs to propose a two-phase ANN model to integrate the results among many models.

A hybrid particle swarm optimization–back-propagation algorithm for feedforward neural network training

The particle swarm optimization algorithm was showed to converge rapidly during the initial stages of a global search, but around global optimum, the search process will become very slow. On the contrary, the gradient descending method can achieve faster convergent speed around global optimum, and at the same time, the convergent accuracy can be higher. So in this paper, a hybrid algorithm combining particle swarm optimization (PSO) algorithm with back-propagation (BP) algorithm, also referred to as PSO–BP algorithm, is proposed to train the weights of feedforward neural network (FNN), the hybrid algorithm can make use of not only strong global searching ability of the PSOA, but also strong local searching ability of the BP algorithm. In this paper, a novel selection strategy of the inertial weight is introduced to the PSO algorithm. In the proposed PSO–BP algorithm, we adopt a heuristic way to give a transition from particle swarm search to gradient descending search. In this paper, we also give three kind of encoding strategy of particles, and give the different problem area in which every encoding strategy is used. The experimental results show that the proposed hybrid PSO–BP algorithm is better than the Adaptive Particle swarm optimization algorithm (APSOA) and BP algorithm in convergent speed and convergent accuracy.