Fuzzy Optimization of an Elevator Mechanism Applying the Genetic Algorithm and Neural Networks

Considering the indefinite character of the value of design parameters and being satisfied with load-bearing capacity and stiffness, the fuzzy optimization mathematical model is set up to minimize the volume of tooth corona of a worm gear in an elevator mechanism. The method of second-class comprehensive evaluation was used based on the optimal level cut set, thus the optimal level value of every fuzzy constraint can be attained; the fuzzy optimization is transformed into the usual optimization. The Fast Back Propagation of the neural networks algorithm are adopted to train feed-forward networks so as to fit a relative coefficient. Then the fitness function with penalty terms is built by a penalty strategy, a neural networks program is recalled, and solver functions of the Genetic Algorithm Toolbox of Matlab software are adopted to solve the optimization model.

Numeral eddy current sensor modelling based on genetic neural network

This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network （GNN） according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness, on-line modelling and high precision. The maximum nonlinearity error can be reduced to 0.037% by using GNN. However, the maximum nonlinearity error is 0.075% using the least square method.

Recovery and grade prediction of pilot plant flotation column concentrate by a hybrid neural genetic algorithm

Today flotation column has become an acceptable means of froth flotation for a fairly broad range of applications, in particular the cleaning of sulfides. Even after having been used for several years in mineral processing plants, the full potential of the flotation column process is still not fully exploited. There is no prediction of process performance for the complete use of available control capabilities. The on-line estimation of grade usually requires a significant amount of work in maintenance and calibration of on-stream analyzers, in order to maintain good accuracy and high availability. These difficulties and the high cost of investment and maintenance of these devices have encouraged the approach of prediction of metal grade and recovery. In this paper, a new approach has been proposed for metallurgical performance prediction in flotation columns using Artificial Neural Network (ANN). Despite of the wide range of applications and flexibility of NNs, there is still no general framework or procedure through which the appropriate network for a specific task can be designed. Design and structural optimization of NNs is still strongly dependent upon the designer's experience. To mitigate this problem, a new method for the auto-design of NNs was used, based on Genetic Algorithm (GA). The new proposed method was evaluated by a case study in pilot plant flotation column at Sarcheshmeh copper plant. The chemical reagents dosage, froth height, air, wash water flow rates, gas holdup, Cu grade in the rougher feed, flotation column feed, column tail and final concentrate streams were used to the simulation by GANN. In this work, multi-layer NNs with Back Propagation (BP) algorithm with 8-17-10-2 and 8- 13-6-2 arrangements have been applied to predict the Cu and Mo grades and recoveries, respectively. The correlation coefficient (R) values for the testing sets for Cu and Mo grades were 0.93, 0.94 and for their recoveries were 0.93, 0.92, respectively. The results discussed in this paper indicate that the proposed model can be used to predict the Cu and Mo grades and recoveries with a reasonable error.

A New Modeling Method Based on Genetic Neural Network for Numeral Eddy Current Sensor

In this paper,we present a method used to the numeral eddy current sensor modeling based on genetic neural network to settle its nonlinear problem.The principle and algorithms of genetic neural network are introduced.In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data.So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network.The nonlinear model has the advantages of strong robustness,on-line scaling and high precision.The maximum nonlinearity error can be reduced to 0.037% using GNN.However,the maximum nonlinearity error is 0.075% using least square method (LMS).

Nonlinear model predictive control based on support vector machine and genetic algorithm

This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used to approximate each output of the controlled plant Then the model is used in MPC control scheme to predict the outputs of the controlled plant.The optimal control sequence is calculated using GA with elite preserve strategy.Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.

GA-BASED PID NEURAL NETWORK CONTROL FOR MAGNETIC BEARING SYSTEMS

In order to overcome the system non-linearity and uncertainty inherent in magnetic bearing systems, a GA（genetic algnrithm）-based PID neural network controller is designed and trained tO emulate the operation of a complete system （magnetic bearing, controller, and power amplifiers）. The feasibility of using a neural network to control nonlinear magnetic bearing systems with unknown dynamics is demonstrated. The key concept of the control scheme is to use GA to evaluate the candidate solutions （chromosomes）, increase the generalization ability of PID neural network and avoid suffering from the local minima problem in network learning due to the use of gradient descent learning method. The simulation results show that the proposed architecture provides well robust performance and better reinforcement learning capability in controlling magnetic bearing systems.

A weighted selection combining scheme for cooperative spectrum prediction in cognitive radio networks

A weighted selection combining （WSC） scheme is proposed to improve prediction accuracy for cooperative spectrum prediction in cognitive radio networks by exploiting spatial diversity. First, a genetic algorithm-based neural network （GANN） is designed to perform spectrum prediction in consideration of both the characteristics of the primary users （PU） and the effect of fading. Then, a fusion selection method based on the iterative self-organizing data analysis （ISODATA） algorithm is designed to select the best local predictors for combination. Additionally, a reliability-based weighted combination rule is proposed to make an accurate decision based on local prediction results considering the diversity of the predictors. Finally, a Gaussian approximation approach is employed to study the performance of the proposed WSC scheme, and the expressions of the global prediction precision and throughput enhancement are derived. Simulation results reveal that the proposed WSC scheme outperforms the other cooperative spectrum prediction schemes in terms of prediction accuracy, and can achieve significant throughput gain for cognitive radio networks.

ANN model of subdivision error based on genetic algorithm

According to the test data of subdivision errors in the measuring cycle of angular measuring system, the characteristics of subdivision errors generated by this system are analyzed. It is found that the subdivision errors are mainly due to the rotary-type inductosyn itself. For the characteristic of cyclical change, the subdivision errors in other measuring cycles can be compensated by the subdivision error model in one measuring cycle. Using the measured error data as training samples, combining GA and BP algorithm, an ANN model of subdivision error is designed. Simulation results indicate that GA reduces the uncertainty in the training process of the ANN model, and enhances the generalization of the model. Compared with the error model based on the least-mean-squared method, the designed ANN model of subdivision errors can achieve higher compensating precision.

Improvement of a Genetic Back Propagation Algorithm and Its Application to Diagnosis in Mechanical Failure

A new improved genetic BP algorithm was put forward in the paper. To determine whether the network falls into local minimum point, a discriminant of local minimum was put forth in the training process of a neural network. A genetic algorithm was used to revise the weights of the neural network if the BP algorithm fell into minimums. The mechanical faults were diagnosed using the algorithm put forward in the paper, which verified the validity of this improved genetic BP algorithm.

我国社区卫生人力资源预测

目的 分析我国社区卫生人力资源发展趋势,以期为健康中国建设提供参考。方法 通过MATLAB R 2018 A建立灰色遗传算法优化(genetic algorithm-back propagation,GA-BP)神经网络组合模型,预测2021—2023年我国社区卫生人力资源,并比较各单预测模型与组合模型预测精度。结果 组合预测模型精度较好,卫生人员和卫生技术人员网络模型的均方误差(mean squared error,MSE) 和平均绝对百分比误差(mean absolute percentage error,MAPE) 的值分别为0.020 6、0.216 2%和0.019 5、0.167 4%,优于单模型预测。模型预测结果合理,我国社区卫生人员数和卫生技术人员数均保持增长趋势,2023年可分别达到71.403 8万人和60.029 0万人。结论 灰色-GA-BP神经网络组合预测模型适合我国社区卫生人力资源预测,随着医疗服务需求量的增加和新型冠状病毒肺炎疫情防控的常态化,社区卫生人力资源发展规模将逐渐提升,应注重各类卫生人才培训,保障社区卫生人员的切身利益,提升社区医疗服务能力。

Model of Land Suitability Evaluation Based on Computational Intelligence

A novel model of land suitability evaluation is built based on computational intelligence (CI). A fuzzy neural network (FNN) is constructed by the integration of fuzzy logic and artificial neural network (ANN). The structure and process of this network is clear. Fuzzy rules (knowledge) are expressed in the model explicitly, and can be self-adjusted by learning from samples. Genetic algorithm (GA) is employed as the learning algorithm to train the network, and makes the training of the model efficient. This model is a self-learning and self-adaptive system with a rule set revised by training.

Winter wheat leaf area index inversion by the genetic algorithms neural network model based on SAR data

The leaf area index(LAI)is an important agroecological physiological parameter affecting vegetation growth.To apply the genetic algorithms neural network model(GANNM)to the remote sensing inversion of winter wheat LAI throughout the growth cycle and based on GaoFen-3 Synthetic aperture radar(GF-3 SAR)images and GaoFen-1 Wide Field of View(GF-1 WFV)images,the Xiangfu District in the east of Kaifeng City,Henan Province,was selected as the testing region.Winter wheat LAI data from five growth stages were combined,and optical and microwave polarization decomposition vegetation index models were used.The backscattering coefficient was extracted by modified water cloud model(MWCM),and the LAI was obtained by MWCM inversion as input factors to construct GANNM to invert LAI.The root mean square error(RMSE)and determination coefficient(R2)were used as evaluation indicators of the model.The fitting accuracy of winter wheat LAI in five growth stages by GANNM inversion was better than that of the BP neural network model;the R2 was higher than 0.8,and RMSE was lower than 0.3,indicating that the model could accurately invert the growth status of winter wheat in five growth stages.

Optimization of press bend forming path of aircraft integral panel

In order to design the press bend forming path of aircraft integral panels,a novel optimization method was proposed, which integrates FEM equivalent model based on previous study,the artificial neural network response surface,and the genetic algorithm.First,a multi-step press bend forming FEM equivalent model was established,with which the FEM experiments designed with Taguchi method were performed.Then,the BP neural network response surface was developed with the sample data from the FEM experiments.Furthermore,genetic algorithm was applied with the neural network response surface as the objective function. Finally,verification was carried out on a simple curvature grid-type stiffened panel.The forming error of the panel formed with the optimal path is only 0.098 39 and the calculating efficiency has been improved by 77%.Therefore,this novel optimization method is quite efficient and indispensable for the press bend forming path designing.

Crashworthiness optimization design of foam-filled tapered decagonal structures subjected to axial and oblique impacts

In this research,crashworthiness of polyurethane foam-filled tapered decagonal structures with different ratios of a/b=0,0.25,0.5,0.75 and 1 was evaluated under axial and oblique impacts.These new designed structures contained inner and outer tapered tubes,and four stiffening plates connected them together.The parameter a/b corresponds to the inner tube side length to the outer tube one.In addition,the space between the inner and outer tubes was filled with polyurethane foam.After validating the finite element model generated in LS-DYNA using the results of experimental tests,crashworthiness indicators of SEA(specific energy absorption)and Fmax(peak crushing force)were obtained for the studied structures.Based on the TOPSIS calculations,the semi-foam filled decagonal structure with the ratio of a/b=0.5 demonstrated the best crashworthiness capability among the studied ratios of a/b.Finally,optimum thicknesses(t1(thickness of the outer tube),t2(thickness of the inner tube),t3(thickness of the stiffening plates))of the selected decagonal structure were obtained by adopting RBF(radial basis function)neural network and genetic algorithm.

Ensemble Prediction of Monsoon Index with a Genetic Neural Network Model

After the consideration of the nonlinear nature changes of monsoon index,and the subjective determination of network structure in traditional artificial neural network prediction modeling,monthly and seasonal monsoon intensity index prediction is studied in this paper by using nonlinear genetic neural network ensemble prediction（GNNEP）modeling.It differs from traditional prediction modeling in the following aspects： （1）Input factors of the GNNEP model of monsoon index were selected from a large quantity of preceding period high correlation factors,such as monthly sea temperature fields,monthly 500-hPa air temperature fields,monthly 200-hPa geopotential height fields,etc.,and they were also highly information-condensed and system dimensionality-reduced by using the empirical orthogonal function（EOF）method,which effectively condensed the useful information of predictors and therefore controlled the size of network structure of the GNNEP model.（2）In the input design of the GNNEP model,a mean generating function（MGF）series of predictand（monsoon index）was added as an input factor;the contrast analysis of results of predic- tion experiments by a physical variable predictor-predictand MGF GNNEP model and a physical variable predictor GNNEP model shows that the incorporation of the periodical variation of predictand（monsoon index）is very effective in improving the prediction of monsoon index.（3）Different from the traditional neural network modeling,the GNNEP modeling is able to objectively determine the network structure of the GNNNEP model,and the model constructed has a better generalization capability.In the case of identical predictors,prediction modeling samples,and independent prediction samples,the prediction accuracy of our GNNEP model combined with the system dimensionality reduction technique of predictors is clearly higher than that of the traditional stepwise regression model using the traditional treatment technique of predictors,suggesting that the GNNEP model opens up a vast range of possibilities for operational weather prediction.

Research on Flashover Voltage Prediction of Catenary Insulator Based on CaSO_(4) Pollution with Different Mass Fraction

Pollution flashover accidents occur frequently in railway OCS in saline-alkali areas.To accurately predict the pollution flashover voltage of insulators,a pollution flashover warning should be made in advance.According to the operating environment of insulators along the Qinghai-Tibet railway,the pollution flashover experiments were designed for the cantilever composite insulator FQBG-25/12.Through the experiments,the flashover voltage under the influence of soluble contaminant density(SCD)of different pollution components,non-soluble deposit density(NSDD),temperature(T),and atmospheric pressure(P)was obtained.On this basis,the GA-BP neural network prediction model was established.P,SCD,NSDD,CaSO_(4) mass fraction(w(CaSO_(4))),and T were taken as input parameters,50%flashover voltage(U_(50%))of the insulator was taken as output parameters.The results showed that the prediction deviation was less than 10%,which meets the basic engineering requirements.The results could not only provide early warning for the anti-pollution flashover work of the railway power supply department,but also be used as an auxiliary contrast to verify the accuracy of the results of the experiments,and provide a theoretical basis for the classification of pollution levels in different regions.

Boron removal from metallurgical grade silicon by slag refining based on GA-BP neural network

In order to investigate the boron removal effect in slag refining process,intermediate frequency furnace was used to purify boron in SiO2-CaO-Na3 AlF6-CaSiO3 slag system at 1,550℃,and back propagation(BP)neural network was used to model the relationship between slag compositions and boron content in SiO2-CaO-Na3 AlF6-CaSiO3 slag system.The BP neural network predicted error is below 2.38%.The prediction results show that the slag composition has a significant influence on boron removal.Increasing the basicity of slag by adding CaO or Na3 AlF6 to CaSiO3-based slag could contribute to the boron removal,and the addition of Na3 AlF6 has a better removal effect in comparison with the addition of CaO.The oxidizing characteristic of CaSiO3 results in the ineffective removal with the addition of SiO2.The increase of oxygen potential(pO2)in the CaO-Na3 AlF6-CaSiO3 slag system by varying the SiO2 proportion can also contribute to the boron removal in silicon ingot.The best slag composition to remove boron was predicted by BP neural network using genetic algorithm(GA).The predicted results show that the mass fraction of boron in silicon reduces from 14.0000×10-6 to0.4366×10-6 after slag melting using 23.12%SiO2-10.44%CaO-16.83%Na3 AlF6-49.61%CaSiO3 slag system,close to the experimental boron content in silicon which is below 0.5×10-6.

Measure oriented training： a targeted approach to imbalanced classification problems

Since the overall prediction error of a classifier on imbalanced problems can be potentially misleading and bi- ased, alternative performance measures such as G-mean and F-measure have been widely adopted. Various techniques in- cluding sampling and cost sensitive learning are often em- ployed to improve the performance of classifiers in such sit- uations. However, the training process of classifiers is still largely driven by traditional error based objective functions. As a result, there is clearly a gap between the measure accord- ing to which the classifier is evaluated and how the classifier is trained. This paper investigates the prospect of explicitly using the appropriate measure itself to search the hypothesis space to bridge this gap. In the case studies, a standard three- layer neural network is used as the classifier, which is evolved by genetic algorithms （GAs） with G-mean as the objective function. Experimental results on eight benchmark problems show that the proposed method can achieve consistently fa- vorable outcomes in comparison with a commonly used sam- pling technique. The effectiveness of multi-objective opti- mization in handling imbalanced problems is also demon- strated.