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.

A Self-organization Mapping Neural Network Algorithm and Its Application to Identify Ecosystem Service Zones

The self-organization mapping （SOM） neural network algorithm is a new method used to identify the ecosystem service zones at regional extent. According to the ecosystem assessment framework of Millennium Ecosystem Assessment （ MA）, this paper develops an indicator system and conducts a spatial cluster analysis at the 1km by I km grid pixel scale with the SOM neural network algorithm to sort the core ecosystem services over the vertical and horizontal dimensions. A case study was carried out in Xilingol League. The ecosystem services in Xilingol League could be divided to six different ecological zones. The SOM neural network algorithm was capable of identifying the similarities among the input data automatically. The research provides both spatially and temporally valuable information targeted sustainable ecosystem management for decision-makers.

Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index

Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the best vegetation indices for estimating maize biomass,(ii)to investigate the relationship between biomass and leaf area index(LAI)at several growth stages,and(iii)to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network(DNN)algorithm.The results showed that biomass was associated with all vegetation indices.The three-band water index(TBWI)was the best vegetation index for estimating biomass and the corresponding R2,RMSE,and RRMSE were 0.76,2.84 t ha−1,and 38.22%respectively.LAI was highly correlated with biomass(R2=0.89,RMSE=2.27 t ha−1,and RRMSE=30.55%).Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm(R2=0.83,RMSE=1.96 t ha−1,and RRMSE=26.43%).Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices(R2=0.91,RMSE=1.49 t ha−1,and RRMSE=20.05%).Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass.Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices(R2=0.87,RMSE=1.84 t ha−1,and RRMSE=24.76%).The DNN algorithm was effective in improving the estimation accuracy of biomass.It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region.

Adaptive control of parallel manipulators via fuzzy-neural network algorithm

This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algorithms （FNNA）. With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric uncertainties are eliminated. FNNA is used to handle model uncertainties and external disturbances. In the proposed control scheme, we consider modifying the weight of fuzzy rules and present these rules to a MIMO system of parallel manipulators with more than three degrees-of-freedom （DoF）. The algorithm has the advantage of not requiring the inverse of the Jacobian matrix especially for the low DoF parallel manipulators. The validity of the control scheme is shown through numerical simulations of a 6-RPS parallel manipulator with three DoF.

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.

Genetic Nelder-Mead neural network algorithm for fault parameter inversion using GPS data

The traditional genetic algorithm(GA)has unstable inversion results and is easy to fall into the local optimum when inverting fault parameters.Therefore,this article considers the combination of GA with other non-linear algorithms in order to improve the inversion precision of GA.This paper proposes a genetic Nelder-Mead neural network algorithm(GNMNNA).This algorithm uses a neural network algorithm(NNA)to optimize the global search ability of GA.At the same time,the simplex algorithm is used to optimize the local search capability of the GA.Through numerical examples,the stability of the inversion algorithm under different strategies is explored.The experimental results show that the proposed GNMNNA has stronger inversion stability and higher precision compared with the existing algorithms.The effectiveness of GNMNNA is verified by the BodrumeKos earthquake and Monte Cristo Range earthquake.The experimental results show that GNMNNA is superior to GA and NNA in both inversion precision and computational stability.Therefore,GNMNNA has greater application potential in complex earthquake environment.

Optimization of a crude distillation unit using a combination of wavelet neural network and line-up competition algorithm

The modeling and optimization of an industrial-scale crude distillation unit （CDU） are addressed. The main spec- ifications and base conditions of CDU are taken from a crude oil refinery in Wuhan, China. For modeling of a com- plicated CDU, an improved wavelet neural network （WNN） is presented to model the complicated CDU, in which novel parametric updating laws are developed to precisely capture the characteristics of CDU. To address CDU in an economically optimal manner, an economic optimization algorithm under prescribed constraints is presented. By using a combination of WNN-based optimization model and line-up competition algorithm （LCA）, the supe- rior performance of the proposed approach is verified. Compared with the base operating condition, it is validat- ed that the increments of products including kerosene and diesel are up to 20% at least by increasing less than 5% duties of intermediate coolers such as second pump-around （PA2） and third Dump-around （PA3）.

Estimation of Weibull Distribution Parameters for Wind Speed Characteristics Using Neural Network Algorithm

Harvesting the power coming from the wind provides a green andenvironmentally friendly approach to producing electricity. To facilitate theongoing advancement in wind energy applications, deep knowledge aboutwind regime behavior is essential. Wind speed is typically characterized bya statistical distribution, and the two-parameters Weibull distribution hasshown its ability to represent wind speeds worldwide. Estimation of Weibullparameters, namely scale (c) and shape (k) parameters, is vital to describethe observed wind speeds data accurately. Yet, it is still a challenging task.Several numerical estimation approaches have been used by researchers toobtain c and k. However, utilizing such methods to characterize wind speedsmay lead to unsatisfactory accuracy. Therefore, this study aims to investigatethe performance of the metaheuristic optimization algorithm, Neural NetworkAlgorithm (NNA), in obtaining Weibull parameters and comparing itsperformance with five numerical estimation approaches. In carrying out thestudy, the wind characteristics of three sites in Saudi Arabia, namely HaferAl Batin, Riyadh, and Sharurah, are analyzed. Results exhibit that NNA hashigh accuracy fitting results compared to the numerical estimation methods.The NNA demonstrates its efficiency in optimizing Weibull parameters at allthe considered sites with correlations exceeding 98.54.

Agricultural Water Resources Utilization and Management under Agricultural Safety Aim Based on Fuzzy Neural Network Algorithm

[Objectives]To explore the agricultural water resources utilization and management under the agricultural safety aim.[Methods]Fuzzy neural network algorithm was adopted.The evaluation model of agricultural water resources utilization and management carrying capacity based on quantitative system was established.[Results]With the remarkable improvement of China's national income,great progress has been made in China's agricultural development.However,in the process of agricultural safety production,the problem of sustainable development has not been noticed,the problem of water resources exceeding the limit bearing capacity frequently occurs.[Conclusions]It is of great significance to effectively solve the problem of water resources utilization and management.In the feasibility test for the algorithm,further tests on various indicators show that the research is feasible.

Multi-Objective Optimization for Hydrodynamic Performance of A Semi-Submersible FOWT Platform Based on Multi-Fidelity Surrogate Models and NSGA-Ⅱ Algorithms

This study delineates the development of the optimization framework for the preliminary design phase of Floating Offshore Wind Turbines(FOWTs),and the central challenge addressed is the optimization of the FOWT platform dimensional parameters in relation to motion responses.Although the three-dimensional potential flow(TDPF)panel method is recognized for its precision in calculating FOWT motion responses,its computational intensity necessitates an alternative approach for efficiency.Herein,a novel application of varying fidelity frequency-domain computational strategies is introduced,which synthesizes the strip theory with the TDPF panel method to strike a balance between computational speed and accuracy.The Co-Kriging algorithm is employed to forge a surrogate model that amalgamates these computational strategies.Optimization objectives are centered on the platform’s motion response in heave and pitch directions under general sea conditions.The steel usage,the range of design variables,and geometric considerations are optimization constraints.The angle of the pontoons,the number of columns,the radius of the central column and the parameters of the mooring lines are optimization constants.This informed the structuring of a multi-objective optimization model utilizing the Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ)algorithm.For the case of the IEA UMaine VolturnUS-S Reference Platform,Pareto fronts are discerned based on the above framework and delineate the relationship between competing motion response objectives.The efficacy of final designs is substantiated through the time-domain calculation model,which ensures that the motion responses in extreme sea conditions are superior to those of the initial design.

Prediction of Properties in Thermomechanically Treated Cu-Cr-Zr Alloy by an Artificial Neural Network

A supervised artificial neural network (ANN) to model the nonlinear relationship between parameters of thermomechanical treatment processes with respect to hardness and conductivity properties was proposed for Cu-Cr-Zr alloy. The improved model was developed by the Levenberg-Marquardt training algorithm. A basic repository on the domain knowledge of thermomechanical treatment processes is established via sufficient data acquisition by the network. The results showed that the ANN system is an effective way and can be successfully used to predict and analyze the properties of Cu-Cr-Zr alloy.

Coal mine safety production forewarning based on improved BP neural network

Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method, adaptive learning rate, particle swarm optimization algorithm, variable weight method and asynchronous learning factor, are used to optimize BP neural network models. Further, the models are applied to a comparative study on coal mine safety warning instance. Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model, and MPSO- BP model can not only effectively reduce the possibility of the network falling into a local minimum point, but also has fast convergence and high precision, which will provide the scientific basis for the forewarnin~ management of coal mine safetv production.

Motion Control of Underwater Vehicle Based on Least Disturbance BP Algorithm

Up to now, some technology of neural networks are developed to solve the non-linearity of researched objects and to implement the adaptive control in many engineering fields, and some good results were achieved. Though it puts some questions over to design application structure with neural networks, it is really unknowable about the study mechanism of those. But, the importance of study ratio is widely realized by many scientists now, and some methods on the modification of that are provided. The main subject is how to improve the stability and how to increase the convergent rate of networks by defining a good form of the study ratio. Here a new algorithm named LDBP (least disturbance BP algorithm) is proposed to calculate the ratio online according to the output errors, the weights of network and the input values. The algorithm is applied to the control of an autonomous underwater vehicle designed by HEU. The experimental results show that the algorithm has good performance and the controller designed based on it is fine.

Artificial neural network approach for rheological characteristics of coal-water slurry using microwave pre-treatment

Detailed experimental investigations were carried out for microwave pre-treatment of high ash Indian coal at high power level(900 W) in microwave oven. The microwave exposure times were fixed at60 s and 120 s. A rheology characteristic for microwave pre-treatment of coal-water slurry(CWS) was performed in an online Bohlin viscometer. The non-Newtonian character of the slurry follows the rheological model of Ostwald de Waele. The values of n and k vary from 0.31 to 0.64 and 0.19 to 0.81 Pa·sn,respectively. This paper presents an artificial neural network(ANN) model to predict the effects of operational parameters on apparent viscosity of CWS. A 4-2-1 topology with Levenberg-Marquardt training algorithm(trainlm) was selected as the controlled ANN. Mean squared error(MSE) of 0.002 and coefficient of multiple determinations(R^2) of 0.99 were obtained for the outperforming model. The promising values of correlation coefficient further confirm the robustness and satisfactory performance of the proposed ANN model.

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.

Optimizing control of coal flotation by neuro-immune algorithm

Coal flotation is widely used to separate commercially valuable coal from the fine ore slurry, and is an industrial process with nonlinear, multivariable, time-varying and long time-delay characteristics. The online detection of ash content of products as the operation performance evaluation in the flotation system is extraordinarily difficult because of the low solid content and numerous micro-bubbles in the slurry. Moreover, it is time-consuming by manual analysis. Consequently, the optimal separation is not usually maintained. A novel technique, called the neuro-immune algorithm (NIA) inspired by the biological nervous and immune systems, is presented in this paper for predicting the ash content of clean coal and performing the optimizing control to the coal flotation system. The proposed algorithm integrates the deeply-studied artificial neural network (ANN) and the developing artificial immune system (AIS). A two-layer back-propagation network was constructed offline based on the historical process data under the best system situation, using five parameters: the flow and the density of raw slurry, the input flows of water, the kerosene and the GF oil, as the inputs and the ash content of clean coal as the output. The immune cell of AIS is made up of six parameters above as the antigen. The cytokine based clone selection algorithm is used to produce the relative antibody. The detailed computation procedures about the hybrid neuro-immune algorithm are minutely discussed. The ash content of clean coal was predicted by NIA using the practical process data s: (308.6 174.7 146.1 43.6 4.0 9.4), and the absolute difference between the actual and computed ash content values was 0.0967%. The optimizing control on NIA was simulated considering two different situations where the ash content of clean coal was controlled downward from 10.00% or upward from 9.20% predicted by ANN to the target value 9.50%. The results indicate that the target ash content and the value of controlling parameters are obtained after several control cycles.

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.

Performance prediction of gravity concentrator by using artificial neural network-a case study

In conventional chromite beneficiation plant, huge quantity of chromite is used to loss in the form of tailing. For recovery these valuable mineral, a gravity concentrator viz. wet shaking table was used.Optimisation along with performance prediction of the unit operation is necessary for efficient recovery.So, in this present study, an artificial neural network(ANN) modeling approach was attempted for predicting the performance of wet shaking table in terms of grade(%) and recovery(%). A three layer feed forward neural network(3:3–11–2:2) was developed by varying the major operating parameters such as wash water flow rate(L/min), deck tilt angle(degree) and slurry feed rate(L/h). The predicted value obtained by the neural network model shows excellent agreement with the experimental values.

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.

A Quantitative Seismic Topographic Effect Prediction Method Based upon BP Neural Network Algorithm and FEM Simulation

Topography can strongly affect ground motion,and studies of the quantification of hill surfaces’topographic effect are relatively rare.In this paper,a new quantitative seismic topographic effect prediction method based upon the BP neural network algorithm and three-dimensional finite element method(FEM)was developed.The FEM simulation results were compared with seismic records and the results show that the PGA and response spectra have a tendency to increase with increasing elevation,but the correlation between PGA amplification factors and slope is not obvious for low hills.New BP neural network models were established for the prediction of amplification factors of PGA and response spectra.Two kinds of input variables’combinations which are convenient to achieve are proposed in this paper for the prediction of amplification factors of PGA and response spectra,respectively.The absolute values of prediction errors can be mostly within 0.1 for PGA amplification factors,and they can be mostly within 0.2 for response spectra’s amplification factors.One input variables’combination can achieve better prediction performance while the other one has better expandability of the predictive region.Particularly,the BP models only employ one hidden layer with about a hundred nodes,which makes it efficient for training.