Fuzzy Q learning algorithm for dual-aircraft path planning to cooperatively detect targets by passive radars

The problem of passive detection discussed in this paper involves searching and locating an aerial emitter by dualaircraft using passive radars. In order to improve the detection probability and accuracy, a fuzzy Q learning algorithrn for dual-aircraft flight path planning is proposed. The passive detection task model of the dual-aircraft is set up based on the partition of the target active radar＇s radiation area. The problem is formulated as a Markov decision process （MDP） by using the fuzzy theory to make a generalization of the state space and defining the transition functions, action space and reward function properly. Details of the path planning algorithm are presented. Simulation results indicate that the algorithm can provide adaptive strategies for dual-aircraft to control their flight paths to detect a non-maneuvering or maneu- vering target.

Intelligent learning technique based-on fuzzy logic for multi-robot path planning

Soccer robot system is a tremendously challenging intelligent system developed to mimic human soccer competition based on the multi discipline research: robotics, intelligent control, computer vision, etc. robot path planning strategy is a very important subject concerning to the performance and intelligence degree of the multi robot system. Therefore, this paper studies the path planning strategy of soccer system by using fuzzy logic. After setting up two fuzziers and two sorts of fuzzy rules for soccer system, fuzzy logic is applied to workspace partition and path revision. The experiment results show that this technique can well enhance the performance and intelligence degree of the system.

Genetic algorithm-fuzzy based dynamic motion planning approach for a mobile robot

Presents the mobile robots dynamic motion planning problem with a task to find an obstacle free route that requires minimum travel time from the start point to the destination point in a changing environment, due to the obstacle’s moving. An Genetic Algorithm fuzzy (GA Fuzzy) based optimal approach proposed to find any obstacle free path and the GA used to select the optimal one, points out that using this learned knowledge off line, a mobile robot can navigate to its goal point when it faces new scenario on line. Concludes with the optimal rule base given and the simulation results showing its effectiveness.

Irrigation Planning with Conjunctive Use of Surface and Groundwater Using Fuzzy Resources

Surface and groundwater are related systems. They can be used conjunctively to maximize the efficient use of available resources. Groundwater may be used to supplement surface water to cope with the irrigation demands to meet the deficits in low rainfall periods. The parameters involved in the present study are groundwater availability, surface water availability, water requirement of crops and crop area. The inclusion of such uncertain parameters leads to accept the decision making process beyond the consideration of economic benefits. In the present study, an irrigation planning model is formulated by considering the conjunctive use of surface and groundwater. The resources in the present model, i.e. the area, surface water and groundwater availability are represented by fuzzy set. The linear membership function is used to fuzzify the objective function and resources. The model is applied to a case study of Jayakwadi project and solved for maximization of the degree of satisfaction (l) which is 0.546.

Two-Phase Multi Objective Fuzzy Linear Programming Approach for Sustainable Irrigation Planning

The objective of the present study is to develop the irrigation planning model and to apply the same in the form of Two-Phase Multi Objective Fuzzy Linear Programming (TPMOFLP) approach for crop planning in command area of Jayakwadi Project Stage I, Maharashtra State, India. The development of TPMOFLP model is on the basis of various Linear Programming (LP) models and Multi Objective Fuzzy Linear Programming (MOFLP) models, these models have been applied for maximization of the Net Benefits (NB), Crop production (CP), Employment Generation (EG) and Manure Utilization (MU) respectively. The significant increase in the value of level of satisfaction (λ) has been found from 0.58 to 0.65 by using the TPMOFLP approach as compare to that of MOFLP model based on maxmin approach. The two-phase approach solution provides NB = 1503.56 Million Rupees, CP = 335729.30 Tons, EG = 29.74 Million Man days and MU = 160233.70 Tons respectively. The proposed model will be helpful for the Decision Maker (DM) to take a decision under conflicting situation while planning for different conflicting objectives simultaneously and has potential to find out an integrated irrigation planning with prime consideration for economic, social and environmental issue.

Improved Fuzzification Method for Multi-Objective Decision-Making and Its Application in Evaluation of Highway Planning

A new fuzzification method for multi-objective decision-making and selective sorting is proposed on the basis of the fuzzy consistent relation, and the specific algorithm is presented. The method is applied to the evaluation of highway planning of Zhanjiang city. To decrease the subjectivity in the process of decision-making, the LOWA operator is introduced, and a discussion on how to select appropriate weights involved in multi-objective sorting is made. It is concluded that it is feasible to apply the fuzzy consistent relation to multi-objective decision-making analysis, and the improved fuzzication method is workable.

Optimal Irrigation Planning and Operation of Multi Objective Reservoir Using Fuzzy Logic

In the present study the MOFLP models have been developed for the optimal cropping pattern planning which maximizes the four objectives such as Net Benefits (NB), Crop Production (CP), Employment Generation (EG) and Manure Utilization (MU) under conflicting situation and also, for maximization of Releases for Irrigation (RI) and Releases for Power (RP) simultaneously under uncertainty by considering the fuzziness in the objective functions. The developed models have been applied using the LINGO 13 (Language for Interactive General Optimization) optimization software to the case study of the Jayakwadi Project Stage-II across Sindhphana River, in the State of Maharashtra India. The various constraints have been taken into consideration like sowing area, affinity to crop, labour availability, manure availability, water availability for optimal cropping pattern planning. Similarly constraints to find the optimal reservoir operating policy are releases for power and turbine capacity, irrigation demand, reservoir storage capacity, reservoir storage continuity. The level of satisfaction for a compromised solution of optimal cropping pattern planning for four conflicting objectives under fuzzy environment is worked out to be λ = 0.68. The MOFLP compromised solution provides NB = 1088.46 (Million Rupees), CP = 241003 (Tons), EG = 23.13 (Million Man days) and MU = 111454.70 (Tons) respectively. The compromised solution for optimal operation of multi objective reservoir yields the level of satisfaction (λ) = 0.533 for maximizing the releases for irrigation and power simultaneously by satisfying the constraint of the system under consideration. The compromised solution provides the optimal releases, i.e. RI = 348.670 Mm3 and RP = 234.285 Mm3 respectively.

Stability of Production Planning Problem with Fuzzy Parameters

The traditional production planning model based upon the famous linear programming formulation has been well known in the literature. The consideration of uncertainty in manufacturing systems supposes a great advance. Models for production planning which do not recognize the uncertainty can be expected to generate inferior planning decisions as compared to models that explicitly account the uncertainty. This paper deals with production planning problem with fuzzy parameters in both of the objective function and constraints. We have a planning problem to maximize revenues net of the production inventory and lost sales cost. The existing results concerning the qualitative and quantitative analysis of basic notions in parametric production planning problem with fuzzy parameters. These notions are the set of feasible parameters, the solvability set and the stability set of the first kind.

A Novel Robot Motion Planning Model based on Visual Navigation and Fuzzy Control

In this paper, we propose the novel robot motion planning model based on the visual navigation and fuzzy control. A robot operating system can be viewed as the mechanical energy converter from the joint space to the global operation space, and the fiexibility of the robot system refi ects the global transformation ability of the whole system. Fuzzy control technology is a kind of fuzzy science, artificial intelligence, knowledge engineering and other disciplines interdisciplinary fields, the theory of strong science and technology, to achieve this fuzzy control technology theory, known as the fuzzy control theory. Besides this, this paper integrates the visual navigation system to construct the better robust methodology which is meaningful.

Fuzzy Logic Based Approach in an Enterprise Resource Planning System for Hydraulic Cylinders Assembly

Trends in modern industry show a tendency towards demassovization of production as a response to the customers＇ specific needs for unique and personalized products. This provokes significant changes in the processes of manufacturing, assembly, and testing The cost of such a type of production can be reduced by employing highly productive reconfigurable equipment with proper software to enable optimization. This paper presents a decision support extension for directing of hydraulic cylinders to assembly-testing lines using fuzzy logic in the Enterprise Resource Planning system of a small size production in a factory in Bulgaria. Different assembly-testing lines are flexibly assigned to the specific cylinder＇s parameters by the developed fuzzy system on the basis of the overlapping of parameters in the hydraulic cylinders classification. The final decision on the line assigned in case of alternatives is made through accounting for the minimal cylinder delay time. The effectiveness of the approach is assessed by simulation. It leads to an increase of the efficiency of the assembly-testing flow lines, a reduction of the time needed for hydraulic cylinders assembling and testing and balanced loading of the modules.

Intelligent Path Planning of Vision-Based Mobile Robot with Fuzzy Approach

The path planning problem for intelligent mobile robots involves two main problems:the represent of task environment including obstacles and the development of a strategy to determine a collision-free route.In this paper, new approaches have been developed to solve these problems.The first problem was solved using the fuzzy system approach,which represent obstacles with a circle.The other problem was overcome through the use of a strategy selector,which chooses the best strategy between velocity control strategy and direction control strategy.

Soft Computing Based Procurement Planning of Time-variable Demand in Manufacturing Systems

Procurement planning with discrete time varying demand is an important problem in Enterprise Resource Planning （ERP）. It can be described using the non-analytic mathematical programming model proposed in this paper. To solve the model we propose to use a fuzzy decision embedded genetic algorithm. The algorithm adopts an order strategy selection to simplify the original real optimization problem into binary ones. Then, a fuzzy decision quantification method is used to quantify experience from planning experts. Thus, decision rules can easily be embedded in the computation of genetic operations. This approach is applied to purchase planning problem in a practical machine tool works, where satisfactory results have been achieved.

Path Planning Approach in Unknown Environment

This paper presents a new algorithm of path planning for mobile robots, which utilises the characteristics of the obstacle border and fuzzy logical reasoning. The environment topology or working space is described by the time-variable grid method that can be further described by the moving obstacles and the variation of path safety. Based on the algorithm, a new path planning approach for mobile robots in an unknown environment has been developed. The path planning approach can let a mobile robot find a safe path from the current position to the goal based on a sensor system. The two types of machine learning： advancing learning and exploitation learning or trial learning are explored, and both are applied to the learning of mobile robot path planning algorithm. Comparison with A＊ path planning approach and various simulation results are given to demonstrate the efficiency of the algorithm. This path planning approach can also be applied to computer games.

Special Wide Area Path Planning Method of an Autonomous Mobile Robot Using GPS

Using sensor and GPS to make a trajectory planning for the stationary obstacle, autonommus mobile robot can asstmae that it is placed at the center of the map, and from the distance information between autonomous mobile robot and obstacles. But in case of active moving obstacle, many components and information need to process since their moving trace should be considered in real time. This paper mobile robot＇s driving algorithm of unknown dynamic envirormaent in order to drive intelligently to destination using ultrasonic and Global Positional Systern （GPS）. Sensors adjusted the placement dependment on driving of robot, and the robot plans the evasion method according to obstacle which are detected by sensors. The robot saves GPS coordinate of complex obstacle. If there are many repeated driving, robot creates new obstacles to the hr, ation by itself. And then it drives to the destination resolving a large range of local minirmnn point If it needs an intelligent circtmtantial decision, a proposed algorithm is suited for effective obstacle avoidance and arrival at the destination by performing simulations.

基于PSR-FUZZY模型的土地利用总体规划实施评价研究

土地利用总体规划的实施评价既是规划实施管理的重要内容,又是新一轮规划修编的重要依据,因此探索有效可行的实施评价的方法对于完善土地利用总体规划的研究体系意义重大。在分析国内有关土地利用规划实施评价方法的基础上,将PSR框架模型与模糊综合评价法结合建立基于PSR-FUZZY模型的土地利用总体规划实施评价的方法,并应用于山西省侯马市。通过研究得出侯马市上一轮土地利用总体规划实施评价的结果为良,基本实现了规划的预期目标。PSR-FUZZY模型同时具备了层次系统系和模糊性,能够很好地应用于土地利用总体规划的实施评价,并且可以根据评价的结果分析原因,能够为新一轮规划的修编提供建议和依据。

基于AHP-FUZZY多层次评判的城市轨道交通线网规划方案综合评价

通过对城市轨道交通线网规划的研究和分析,指出城市轨道交通已成为当前大城市交通发展的一项重要内容,在此基础上,建立线网规划的分层评价指标体系.针对现有综合评价方法的不足,将层次分析法和模糊综合评价法相结合,建立了多层次模糊综合评价模型.利用该模型对某市城市轨道交通线网规划方案进行了综合评价,证明所建立的评价模型能利用全部信息,使那些表面上看起来复杂的方案能够有序排列出优劣次序,且能使经验和理论结合起来.排序结果能够比较客观公正地反映城市轨道线网规划综合评价的实际情况,可以为城市轨道交通网络规划方案的评价及方案优选服务,避免因主观原因而造成的评价偏差.

基于Fuzzy-AHP的飞机客舱内环境设计评价

在确定飞机客舱内饰设计评价指标的基础上,运用Fuzzy AHP综合评价法,实现了定性指标的定量转换,建立了飞机客舱内饰设计评价分析模型,克服了评价的主观性和随意性,实现了对飞机客舱内饰设计的科学评价,并编制了飞机客舱内饰设计评价系统软件加以论证。

AHP-FUZZY法论证采煤塌陷地村庄搬迁规划选址的研究

以宿州市埇桥区采煤沉陷区村庄搬迁规划为例,应用AHP-FUZZY法论证采煤塌陷地村庄搬迁规划选址的优劣程度,通过对搬迁选址评价指标体系的设定和模糊综合分析,得出该搬迁规划方案是在顾及各方面影响因素后所得出的最优方案。

Machine tool selection based on fuzzy evaluation and optimization of cutting parameters

The paper analyzes the factors influencing machine tool selection. By using fuzzy mathematics theory, we establish a theorietical model for optimal machine tool selection considering geometric features, clamping size, machining range, machining precision and surface roughness. By means of fuzzy comprehensive evaluation method, the membership degree of machine tool selection and the largest comprehensive evaluation index are determined. Then the reasonably automatic selection of machine tool is realized in the generative computer aided process planning （CAPP） system. Finally, the finite element model based on ABAQUS is established and the cutting process of machine tool is simulated. According to the theoretical and empirical cutting parameters and the curve of surface residual stress, the optimal cutting parameters can be determined.

Mathematical Modeling and a Multiswarm Collaborative Optimization Algorithm for Fuzzy Integrated Process Planning and Scheduling Problem

Considering both process planning and shop scheduling in manufacturing can fully utilize their complementarities,resulting in improved rationality of process routes and high-quality and efficient production. Hence,the study of Integrated Process Planning and Scheduling (IPPS) has become a hot topic in the current production field. However,when performing this integrated optimization,the uncertainty of processing time is a realistic key point that cannot be neglected. Thus,this paper investigates a Fuzzy IPPS (FIPPS) problem to minimize the maximum fuzzy completion time. Compared with the conventional IPPS problem,FIPPS considers the fuzzy process time in the uncertain production environment,which is more practical and realistic. However,it is difficult to solve the FIPPS problem due to the complicated fuzzy calculating rules. To solve this problem,this paper formulates a novel fuzzy mathematical model based on the process network graph and proposes a MultiSwarm Collaborative Optimization Algorithm (MSCOA) with an integrated encoding method to improve the optimization. Different swarms evolve in various directions and collaborate in a certain number of iterations. Moreover,the critical path searching method is introduced according to the triangular fuzzy number,allowing for the calculation of rules to enhance the local searching ability of MSCOA. The numerical experiments extended from the well-known Kim benchmark are conducted to test the performance of the proposed MSCOA. Compared with other competitive algorithms,the results obtained by MSCOA show significant advantages,thus proving its effectiveness in solving the FIPPS problem.