NON-LINEAR DYNAMIC MODEL RETRIEVAL OF SUBTROPICAL HIGH BASED ON EMPIRICAL ORTHOGONAL FUNCTION AND GENETIC ALGORITHM

Aiming at the difficulty of accurately constructing the dynamic model of subtropical high, based on the potential height field time series over 500 hPa layer of T106 numerical forecast products, by using EOF（empirical orthogonal function） temporal-spatial separation technique, the disassembled EOF time coefficients series were regarded as dynamical model variables, and dynamic system retrieval idea as well as genetic algorithm were introduced to make dynamical model parameters optimization search, then, a reasonable non-linear dynamic model of EOF time-coefficients was established. By dynamic model integral and EOF temporal-spatial components assembly, a mid-/long-term forecast of subtropical high was carried out. The experimental results show that the forecast results of dynamic model are superior to that of general numerical model forecast results. A new modeling idea and forecast technique is presented for diagnosing and forecasting such complicated weathers as subtropical high.

THE APPLICATION OF GENETIC ALGORITHM IN NON-LINEAR INVERSION OF ROCK MECHANICS PARAMETERS

The non-linear inversion of rock mechanics parameters based on genetic algorithm is presented. The principIe and step of genetic algorithm is also given. A brief discussion of this method and an application example is presented at the end of this paper. From the satisfied result, quick, convenient and practical new approach is developed to solve this kind of problems.

An Innovative Genetic Algorithms-Based Inexact Non-Linear Programming Problem Solving Method

In this paper, an innovative Genetic Algorithms (GA)-based inexact non-linear programming (GAINLP) problem solving approach has been proposed for solving non-linear programming optimization problems with inexact information (inexact non-linear operation programming). GAINLP was developed based on a GA-based inexact quadratic solving method. The Genetic Algorithm Solver of the Global Optimization Toolbox (GASGOT) developed by MATLABTM was adopted as the implementation environment of this study. GAINLP was applied to a municipality solid waste management case. The results from different scenarios indicated that the proposed GA-based heuristic optimization approach was able to generate a solution for a complicated nonlinear problem, which also involved uncertainty.

Nonlinear Rayleigh wave inversion based on the shuffled frog-leaping algorithm

At present, near-surface shear wave velocities are mainly calculated through Rayleigh wave dispersion-curve inversions in engineering surface investigations, but the required calculations pose a highly nonlinear global optimization problem. In order to alleviate the risk of falling into a local optimal solution, this paper introduces a new global optimization method, the shuffle frog-leaping algorithm （SFLA）, into the Rayleigh wave dispersion-curve inversion process. SFLA is a swarm-intelligence-based algorithm that simulates a group of frogs searching for food. It uses a few parameters, achieves rapid convergence, and is capability of effective global searching. In order to test the reliability and calculation performance of SFLA, noise-free and noisy synthetic datasets were inverted. We conducted a comparative analysis with other established algorithms using the noise-free dataset, and then tested the ability of SFLA to cope with data noise. Finally, we inverted a real-world example to examine the applicability of SFLA. Results from both synthetic and field data demonstrated the effectiveness of SFLA in the interpretation of Rayleigh wave dispersion curves. We found that SFLA is superior to the established methods in terms of both reliability and computational efficiency, so it offers great potential to improve our ability to solve geophysical inversion problems.

A Novel Nonlinear Algorithm for Typhoon Cloud Image Enhancement

A novel nonlinear gray transform method is proposed to enhance the contrast of a typhoon cloud image.Generally,the typhoon cloud image obtained by a satellite cannot be directly used to make an accurate prediction of the typhoon＇s center or intensity because the contrast of the received typhoon cloud image may be bad.Our aim is to extrude the typhoon＇s eye in the typhoon cloud image.A normalized arc-tangent transformation operation is designed to enhance global contrast of the typhoon cloud image.Differential evolution algorithm is used to choose the optimal nonlinear transform parameter.Finally,geodesic activity contour model is used to extract the typhoon＇s eye to verify the performance of the proposed method.Experimental results show that the proposed method can efficiently enhance the global contrast of the typhoon cloud image while greatly extruding the typhoon＇s eye.

An Adaptive Non-Linear Map and Its Application

A novel adaptive non-linear mapping （ANLM）, integrating an adaptive mapping error （AME） with a chaosgenetic algorithm （CGA） including chaotic variable, was proposed to overcome the deficiencies of non-linear mapping （NLM）. The value of AME weight factor is determined according to the relative deviation square of distance between the two mapping points and the corresponding original objects distance. The larger the relative deviation square between two distances is, the larger the value of the corresponding weight factor is. Due to chaotic mapping operator, the evolutional process of CGA makes the individuals of subgenerations distributed ergodieally in the defined space and circumvents the premature of the individuals of subgenerations. The comparison results demonstrated that the whole performance of CGA is better than that of traditional genetic algorithm. Furthermore, a typical example of mapping eight-dimenslonal olive oil samples onto two-dimensional plane was employed to verify the effectiveness of ANLM. The results showed that the topology-preserving map obtained by ANLM can well represent the classification of original objects and is much better than that obtained by NLM.

An Evolutionary Firefly Algorithm, Goal Programming Optimization Approach for Setting the Osmotic Dehydration Parameters of Papaya

An evolutionary nature-inspired Firefly Algorithm (FA) is employed to set the optimal osmotic dehydration parameters in a case study of papaya. In the case, the functional form of the dehydration model is established via a response surface technique with the resulting optimization formulation being a non-linear goal programming model. For optimization, a computationally efficient, FA-driven method is employed and the resulting solution is shown to be superior to those from previous approaches for determining the osmotic process parameters. The final component of this study provides a computational experimentation performed on the FA to illustrate the relative sensitivity of this evolutionary metaheuristic approach over a range of the two key parameters that most influence its running time-the number of iterations and the number of fireflies. This sensitivity analysis revealed that for intermediate-to-high values of either of these two key parameters, the FA would always determine overall optimal solutions, while lower values of either parameter would generate greater variability in solution quality. Since the running time complexity of the FA is polynomial in the number of fireflies but linear in the number of iterations, this experimentation shows that it is more computationally practical to run the FA using a “reasonably small” number of fireflies together with a relatively larger number of iterations than the converse.

FBD算法电流检测的滞后误差建模

作为有源滤波器运行的一个重要环节,电流检测的结果决定了有源滤波器的工作性能。常用电流检测方法的结果都存在一定的滞后误差。本文对FBD法电流检测过程进行了分析,指出了造成滞后误差的原因,并且建立了定量分析的模型,推导出了计算公式。在此基础上提出了通过引入校正环节和减少计算量的方法改进FBD算法,从而减少电流检测的滞后误差,并通过仿真和试验对该方法进行了验证。该方法对于减少电流检测的滞后误差,提高补偿效果具有明显的意义。

基于遗传小波神经网络的压力传感器的非线性校正研究

为了消除压力传感器受非目标参量的影响而呈现的非线性特性,利用小波神经网络来完成压力传感器的非线性校正.利用遗传算法对小波神经网络权阈值优化,以提高网络精确度和训练速度,设计了遗传优化小波神经网络,将该网络用于压力传感器的非线性校正.仿真结果表明该方法能有效消除非目标参量对传感器输出结果的影响.压力传感器的精度和准确度都得到提高.该系统不但可以用于各类传感器的非线性校正,还可用于其它类似系统.且设计、实现简单,适于工程应用,具有实际应用价值.

非视距环境下改进的到达时间差定位算法

经典的定位算法都是基于无线环境为视距环境(LOS),但在实际信道环境中,由于存在反射、折射等从而导致传播环境为非视距的情况,因此经典定位算法应用于实际信道环境中必然会导致较大的定位误差。提出一种消除非视距影响的TDOA算法,本算法采用校正因子的方法,通过逐步的迭代,从而消除非视距的影响,仿真表明,在非视距环境下,该算法的确比常规算法能够提高定位精度,且运算量较小。

基于VB的嵌入式温控系统

介绍了一种数字型PID嵌入式温控式系统。该系统由PC104、传感电路、变送器电路、可控硅控制电路及PID软件功能包和非线性校正软件功能包组成,具有实时监控和自动调整温度等功能。重点介绍了系统的软件设计部分,即用软件实现了PID算法及传感器的非线性校正的方法,系统采用可视化编程语言VB进行编程。

Optimal variable structure control with sliding modes for unstable processes

In this work,a variable structure control(VSC)technique is proposed to achieve satisfactory robustness for unstable processes.Optimal values of unknown parameters of VSC are obtained using Whale optimization algorithm which was recently reported in literature.Stability analysis has been done to verify the suitability of the proposed structure for industrial processes.The proposed control strategy is applied to three different types of unstable processes including non-minimum phase and nonlinear systems.A comparative study ensures that the proposed scheme gives superior performance over the recently reported VSC system.Furthermore,the proposed method gives satisfactory results for a cart inverted pendulum system in the presence of external disturbance and noise.

Research on improved active disturbance rejection control of continuous rotary motor electro-hydraulic servo system

In order to meet the precision requirements and tracking performance of the continuous rotary motor electro-hydraulic servo system under unknown strong non-linear and uncertain strong disturbance factors,such as dynamic uncertainty and parameter perturbation,an improved active disturbance rejection control(ADRC)strategy was proposed.The state space model of the fifth order closed-loop system was established based on the principle of valve-controlled hydraulic motor.Then the three parts of ADRC were improved by parameter perturbation and external disturbance;the fast tracking differentiator was introduced into linear and non-linear combinations;the nonlinear state error feedback was proposed using synovial control;the extended state observer was determined by nonlinear compensation.In addition,the grey wolf algorithm was used to set the parameters of the three parts.The simulation and experimental results show that the improved ADRC can realize the system frequency 12 Hz when the tracking accuracy and response speed meet the requirements of double ten indexes,which lay foundation for the motor application.

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.

基于Adams-Bashforth-Moulton预估校正法的NURBS插补算法研究

针对传统的基于连续微小线段的插补方法在复杂零件型面加工中具有插补误差大和速度波动大等不足,对NURBS曲线的插补原理、插补参数计算、速度波动等方面进行了研究,结合加工弓高误差、法向加速度、法向加加速度等约束条件进行了速度规划,提出了一种基于Adams-Bashforth-Moulton(ABM)预估校正法的NURBS曲线插补算法,阐述了基于Adams-Bashforth-Moulton法的参数预估原理,对基于速度波动控制的Newton-Rapson迭代插补参数校正原理进行说明。最后在Matlab平台上仿真验证了该算法在插补速度波动控制方面的优越性。

Pyrolysis Characteristics and Kinetics of Municipal Solid Waste

Based on a systemic survey, the pyrolysis characteristics and apparent kinetics of the municipal solid waste （ MSW） under different conditions were researched using a special pyrolysis reactor, which could overcome the disadvantage of thermo-gravimetric analyzer. The thermal decomposition behaviour of MSW was investigated using thermo-gravimetric （ TG ） analysis at rates of 4.8,6.6,8.4, 12.0 and 13. 2 K/min. The pyrolysis characteristics of MSW were also studied in different function districts. The pyrolysis of MSW is a complex reaction process and three main stages are found according to the results. The first stage represents the degradation of cellulose and hemicellulose, with the maximum degradation rate occuring at 150℃ -200 ℃： the second stage represents dehydrochlorination and depolymerization of intermediate products and the differential thermogravimetric （ DTG ） curves have shoulder peaks at about 300℃： the third stage is the decomposition of the residual big molecular organic substance and lignin at 400 ℃- 600 ℃. Within the range of given experimental conditions, the results of non-linear fitting algorithm and experiment are in agreement with each other and the correlation coefficients are over0. 99. The kinetic characteristics are concerned with the material component and heating rate. The activation energy of reaction decreases with the increase of heating rate.

A non-linear spatial hearing model based on bases pursuit algorithm

In the research on spatial hearing and realization of virtual auditory space,it is important to effectively model the head-related transfer functions(HRTFs)or head-related impulse responses(HRIRs).In our study,we managed to carry out adaptive non-linear approximation in the field of wavelet transformation.The results show that the HRIRs’adaptive non-linear approximation model is a more effective data reduction model,is faster,and is 5 dB on average better than the traditional principal component analysis(PCA)(Karhunen-Loève transform)model based on relative mean square error(MSE)criterion.Furthermore,we also discussed the best bases’choice for the time-frequency representation of HRIRs,and the results show that local cosine bases are more propitious to HRIRs’adaptive approximation than wavelet and wavelet packet base.However,the improved effect of local cosine bases is not distinct.Here,for the sake of modeling the HRIRs more truthfully,we consider choosing optimal time-frequency atoms from redundant dictionary to decompose this kind of signals of HRIRs and achieve better results than all the previous models.

Hybrid-integer algorithm for a multi-objective optimal home energy management system

Most of the energy produced in the world is consumed by commercial and residential buildings.With the growth in the global economy and world demographics,this energy demand has become increasingly important.This has led to higher unit electricity prices,frequent stresses on the main electricity grid and carbon emissions due to inefficient energy management.This paper presents an energy-consumption management system based on time-shifting of loads according to the dynamic day-ahead electricity pricing.This simultaneously reduces the electricity bill and the peaks,while maintaining user comfort in terms of the operating waiting time of appliances.The proposed optimization problem is formulated mathematically in terms of multi-objective integer non-linear programming,which involves constraints and consumer preferences.For optimal scheduling,the management problem is solved using the hybridization of the particle swarm optimization algorithm and the branch-and-bound algorithm.Two techniques are proposed to manage the trade-off between the conflicting objectives.The first technique is the Pareto-optimal solutions classification using supervised learning methods.The second technique is called the lexicographic method.The simulations were performed based on residential building energy consumption,time-of-use pricing(TOU)and critical peak pricing(CPP).The algorithms were implemented in Python.The results of the current work show that the proposed approach is effective and can reduce the electricity bill and the peak-to-average ratio(PAR)by 28% and 49.32%,respectively,for the TOU tariff rate,and 48.91% and 47.87% for the CPP tariff rate by taking into account the consumer’s comfort level.

Experimental and numerical investigation of design optimization of a partial admitted supersonic turbine

To obtain a high specific work output,the large pressure ratios across the turbine are required.This can be achieved using a supersonic turbine.When the fluid mass flow is low,the impulse kind of one or two stages supersonic turbine is employed.To prevent losses due to low blade aspect ratio and issues related to manufacturing and industrial problems,the turbine is used in partial admission conditions.Studies show that the turbine efficiency is highly dependent on the amount of partial admission coefficient.The turbine efficiency in full admission is high,but the use of partial admission lowers the additional losses.Therefore,there will be a degree of partial admission in which the turbine will have the highest efficiency.The aim of this work is to achieve the optimum partial admission for a special impulse turbine as a case study.Therefore,in the beginning,an appropriate model of losses is presented.Then,using a nonlinear design optimization code,the partial admission of an impulse supersonic turbine is optimized.This code is written using a genetic algorithm.Then,using three-dimensional numerical analysis,the optimal model will be selected.In the optimization problem,the turbine efficiency is the objective function.The amount of design parameters and constraints used in this process are ten and eight,respectively.After the optimization process,prototypes of designed and modified turbines are made and tested.Test results were compared and analyzed.The results showed that the turbine efficiency is improved between 2.5%and 3%depending on various operation conditions.

Nutrient optimization for plant growth in Aquaponic irrigation using Machine Learning for small training datasets

With the recent trends in urban agriculture and climate change,there is an emerging need for alternative plant culture techniques where dependence on soil can be eliminated.Hydroponic and aquaponic growth techniques have proven to be viable alternatives,but the lack of efficient and optimal practices for irrigation and nutrient supply limits its applications on a large-scale commercial basis.The main purpose of this research was to develop statistical methods and Machine Learning algorithms to regulate nutrient concentrations in aquaponic irrigation water based on plant needs,for achieving optimal plant growth and promoting broader adoption of aquaponic culture on a commercial scale.One of the key challenges to developing these algorithms is the sparsity of data which requires the use of Bolstered error estimation approaches.In this paper,several linear and non-linear algorithms trained on relatively small datasets using Bolstered error estimation techniques were evaluated,for selecting the best method in making decisions regarding the regulation of nutrients in hydroponic environments.After repeated tests on the dataset,it was decided that Semi-Bolstered Resubstitution Error estimation technique works best in our case using Linear Support Vector Machine as the classifier with the value of penalty parameter set to one.A set of recommended rules have been prescribed as a Decision Support System,using the output of the Machine Learning algorithm,which have been tested against the results of the baseline model.Further,the positive impact of the recommended nutrient concentrationson plant growth in aquaponic environments has been elaborately discussed.