Search results

To accurately forecast logistics freight volume plays a vital part in rational planning formulation for a country. The purpose of this paper is to contribute to developing a novel combination forecasting model to predict China’s logistics freight volume, in which an improved PSO-BP neural network is proposed to determine the combination weights.

Since BP neural network has the ability of learning, storing, and recalling information that given by individual forecasting models, it is effective in determining the combination weights of combination forecasting model. First, an improved PSO based on simulated annealing method and space-time adjustment strategy (SAPSO) is proposed to solve out the connection weights of BP neural network, which overcomes the problems of local optimum traps, low precision and poor convergence during BP neural network training process. Then, a novel combination forecast model based on SAPSO-BP neural network is established.

Simulation tests prove that the proposed SAPSO has better convergence performance and more stability. At the same time, combination forecasting models based on three types of BP neural networks are developed, which rank as SAPSO-BP, PSO-BP and BP in accordance with mean absolute percentage error (MAPE) and convergent speed. Also the proposed combination model based on SAPSO-BP shows its superiority, compared with some other combination weight assignment methods.

SAPSO-BP neural network is an original contribution to the combination weight assignment methods of combination forecasting model, which has better convergence performance and more stability.

Viscosity is an important basic physical property of liquid solders. However, because of the very complex nonlinear relationship between the viscosity of the liquid ternary Sn-based lead-free solder and its determinants, a theoretical model for the viscosity of the liquid Sn-based solder alloy has not been proposed. This paper aims to address the viscosity issues that must be considered when developing new lead-free solders.

A BP neural network model was established to predict the viscosity of the liquid alloy and the predicted values were compared with the corresponding experimental data in the literature data. At the same time, the BP neural network model is compared with the existing theoretical model. In addition, a mathematical model for estimating the melt viscosity of ternary tin-based lead-free solders was constructed using a polynomial fitting method.

A reasonable BP neural network model was established to predict the melt viscosity of ternary tin-based lead-free solders. The viscosity prediction of the BP neural network agrees well with the experimental results. Compared to the Seetharaman and the Moelwyn–Hughes models, the BP neural network model can predict the viscosity of liquid alloys without the need to calculate the relevant thermodynamic parameters. In addition, a simple equation for estimating the melt viscosity of a ternary tin-based lead-free solder has been proposed.

The study identified nine factors that affect the melt viscosity of ternary tin-based lead-free solders and used these factors as input parameters for BP neural network models. The BP neural network model is more convenient because it does not require the calculation of relevant thermodynamic parameters. In addition, a mathematical model for estimating the viscosity of a ternary Sn-based lead-free solder alloy has been proposed. The overall research shows that the BP neural network model can be well applied to the theoretical study of the viscosity of liquid solder alloys. Using a constructed BP neural network to predict the viscosity of a lead-free solder melt helps to study the liquid physical properties of lead-free solders that are widely used in electronic information.

Mechanical products usually require deterministic finite element analysis in the design phase to determine whether their structures meet the requirements. However, deterministic design ignores the influence of uncertainties in the design and manufacturing process of mechanical products, leading to the problem of a lack of design safety or excessive redundancy in the design. In order to improve the accuracy and rationality of the design results, a robust design method for structural reliability based on an active-learning marine predator algorithm (MPA)–backpropagation (BP) neural network is proposed.

The MPA was used to obtain the optimal weights and thresholds of a BP neural network, and an active-learning function applicable to neural networks was proposed to efficiently improve the prediction performance of the BP neural network. On this basis, a robust optimization design method for mechanical product reliability based on the active-learning MPA-BP model was proposed. Random moving quadrilateral sampling was used to obtain the sample points required for training and testing of the neural network, and the reliability sensitivity corresponding to each sample point was calculated by subset simulated significant sampling (SSIS). The total mass of the mechanical product and the structural reliability sensitivity of the trained active-learning MPA-BP model output were taken as the optimization objectives, and a multi-objective reliability-robust optimization design model was constructed, which was solved by the second-generation non-dominated ranking genetic algorithm (NSGA-II). Then, the dominance function was used in the obtained Pareto solution set to make a dominance-seeking decision to obtain the final reliability-robust optimization design solution. The feasibility of the proposed method was verified by a reliability-robust optimization design example of the bogie frame.

The prediction error of the active-learning MPA-BP neural network was smaller than those of the particle swarm optimization (PSO)-BP, marine predator algorithm (MPA)-BP and genetic algorithm (GA)-BP neural networks under the same basic parameter settings of the algorithm, which indicated that the improvement strategy proposed in this paper improved the prediction accuracy of the BP neural network. To ensure the reliability of the bogie frame, the reliability sensitivity and total mass of the bogie frame were reduced, which not only realized the lightweight design of the bogie frame, but also improved the reliability and robustness of the bogie.

The MPA algorithm with a higher optimization efficiency was introduced to find the weights and thresholds of the BP neural network. A new active-learning function was proposed to improve the prediction accuracy of the MPA-BP neural network.

This paper aims to predict the 28-day compressive strength of recycled thermal insulation concrete more accurately.

The initial weights and thresholds of BP neural network are improved by genetic algorithm on MATLAB 2014 a platform.

Genetic algorithm–back propagation (GA-BP) neural network is more stable. The generalization performance of the complex is better.

The GA-BP neural network based on the training sample data can better realize the strength prediction of recycled aggregate thermal insulation concrete and reduce the complex orthogonal experimental process. GA-BP neural network is more stable. The generalization performance of the complex is better.

The purpose of this paper is to solve the problem that the analytic solution model of spatial three-dimensional coordinate measuring system based on dual-position sensitive detector (PSD) is complex and its precision is not high.

A new three-dimensional coordinate measurement algorithm by optimizing back propagation (BP) neural network based on genetic algorithm (GA) is proposed. The mapping relation between three-dimensional coordinates of space points in the world coordinate system and light spot coordinates formed on dual-PSD has been built and applied to the prediction of three-dimensional coordinates of space points.

The average measurement error of three-dimensional coordinates of space points at three-dimensional coordinate measuring system based on dual-PSD based on GA-BP neural network is relatively small. This method does not require considering the lens distortion and the non-linearity of PSD. It has simple structure and high precision and is suitable for three-dimensional coordinate measurement of space points.

A new three-dimensional coordinate measurement algorithm by optimizing BP neural network based on GA is proposed to predict three-dimensional coordinates of space points formed on three-dimensional coordinate measuring system based on dual-PSD.

For comfort evaluation of underwear pressure, this paper proposes an improved GA algorithm to optimize the weight and threshold of BP neural network, namely PSO-GA-BP neural network prediction model.

The objective parameters of underwear, body shape data, skin deformation and other data are selected for simulation experiments to predict the objective pressure and subjective evaluation in dynamic and static state. Compared with the prediction results of BP neural network prediction model, GA-BP neural network prediction model and PSO-BP neural network prediction model, the performance of each prediction model is verified.

The results show that the BP neural network model optimized by PSO-GA algorithm can accelerate the convergence speed of the neural network and improve the prediction accuracy of underwear pressure.

PSO-GA-BP model provides data support for underwear design, production and processing and has guiding significance for consumers to choose underwear.

This paper aims to build a legal intelligent auxiliary discretionary system for predicting the penalty and damage compensation values. After extensively considering current the characteristics of the current Chinese legal system, a practical legal intelligent auxiliary discretionary system based on genetic algorithm-backpropagation (GA-BP) neural network (NN) is proposed herein.

An experiment is designed to analyze cases involving mental anguish compensation in medical disputes, and a Chinese legal intelligent auxiliary discretionary adviser system is built based on a GA-BP NN. Because BP neural networks perform well for nonlinear problems and GAs can improve their ability to find optimal values, and accelerate their convergence, a combined GA–BP algorithm is used. In addition, an ontology is used to reduce the semantic ambiguities and extract the implied semantic information.

We confirm that a case-based legal intelligent auxiliary discretionary adviser system based on a GA-BP NN and ontology techniques has good performance in prediction. By predicting the mental anguish compensation values, the legal intelligent auxiliary discretionary adviser system can help judges to handle cases more quickly and ordinary people to discover the suggested compensation or penalty. In contrast to BP NN or SVM, the result seems more close to the actual compensation rate.

Recently, smart court has been developed in China; the purpose of which is to build the legal advice system for improving judicial justice and reducing differences in sentencing. A practical legal advice system is an urgent requirement for the judiciary.

This paper presents a study of a case-based legal intelligent auxiliary discretionary adviser system based on a GA-BP NN and ontology techniques. The findings offer advice to optimize legal intelligent auxiliary discretionary adviser systems for mental anguish compensation in medical disputes.

To improve the robustness of missile control system and reduce the error, a missile attitude adaptive control method based on active disturbance rejection control technology (ADRC) and BP neural network is innovatively proposed.

ADRC improves the performance of the missile control system by estimating and eliminating the total disturbance of the system. BP neural network adjusts the parameters of ADRC controller according to the state of the system to realize adaptive control. Based on the control system and missile dynamics model, the convergence analysis of the extended state observer and the stability analysis of the closed-loop system after embedding BP neural network are given.

The simulation results show that the adaptive control method can adjust the coefficient of error feedback rate according to the system input, output and error change rate, which accelerates the response speed of missile attitude angle and reduces the attitude angle error.

BP–ADRC further improves the robustness and environmental adaptability of the missile control system. The BP–ADRC control method proposed in this paper is proved feasible.

Different from the traditional ADRC, the BP–ADRC feedback signal proposed in this paper uses the output signal and its rate of the closed-loop system instead of the system state quantity estimated by extended state observer (ESO). This innovative method combined with BP neural network can make the system output meet the requirements when ESO has errors in the estimation of missile dynamics model.

This study aims to the three major problems of low cleaning efficiency, high labor intensity and difficult to evaluate the cleaning effect for manual insulators cleaning in ultra high voltage (UHV) converter station, the purpose of this paper is to propose a basic configuration of UHV vertical insulator cleaning robot with multi-freedom-degree mechanical arm system on mobile airborne platform and its innovation cleaning operation motion planning.

The main factors affecting the insulators cleaning effect in the operation process have been analyzed. Because of the complex coupling relationship between the influencing factors and the insulators cleaning effect, it is difficult to establish its analytical mathematical model. Combining the non-linear mapping and approximation characteristics of back propagation (BP) neural network, the insulator cleaning effect evaluation can be abstracted as a non-linear approximation process from actual cleaning effect to ideal cleaning effect. An evaluation method of robot insulator cleaning effect based on BP neural network has been proposed.

Through the BP neural network training, the robot cleaning control parameters can be obtained and used in the robot online operation control, so that the better cleaning effect can be also obtained. Finally, a physical prototype of UHV vertical insulator cleaning robot has been developed, and the effectiveness and engineering practicability of the proposed robot configuration, cleaning effect evaluation method are all verified by simulation experiments and field operation experiments. At the same time, this method has the remarkable characteristics of sound versatility, strong adaptability, easy expansion and popularization.

An UHV vertical insulator cleaning robot operation system platform with multi-arm system on airborne platform has been proposed. Through the coordinated movement of the manipulator each joint, the manipulator can be positioned to the insulator strings, and the insulator can be cleaned by two pairs high-pressure nozzles located at the double manipulator. The influence factors of robot insulator cleaning effect have been analyzed. The BP neural network model of insulator cleaning effect evaluation has been established. The evaluation method of robot insulator cleaning effect based on BP neural network has also been proposed, and the corresponding evaluation result can be obtained through the network training. Through the system integration design, the robot physical prototype has been developed. For the evaluation of other operation effects of power system, the validity and engineering practicability of the robot mechanism, motion planning and the method for evaluating the effect of robot insulator cleaning have been verified by simulation and field operation experiments.

The purpose of this paper is to study, a bridged-T topology with inductorless passive network used as a bandpass (BP) negative group delay (NGD) function.

The BP NGD topology under study is composed of an inductorless passive resistive capacitive network. The circuit analysis is elaborated from the equivalent impedance matrix. Then, the analytical model of the C-shunt bridged-T topology voltage transfer function is established. The BP NGD analysis of the considered topology is developed in function of the bridged-T parameters. The NGD properties and characterizations of the proposed topology are analytically expressed. Moreover, the relevance of the BP NGD theory is verified with the design and fabrication of surface mounted device components-based proof-of-concept (PoC).

From measurement results, the BP NGD network with −151 ns at the center frequency of 1 MHz over −6.6 dB attenuation is in very good agreement with the C-shunt bridged-T PoC.

This paper develops a mathematical modeling theory and measurement of a C-shunt bridged-T network circuit.