基于FIS的缓倾斜层状薄矿床采矿方法优选模型

为了减小人的主观经验对采矿方法决策的影响,确保不确定性和不完整性信息的有效利用,采用符合人们思维和语言表达习惯的模糊推理系统,提出一种基于FIS的缓倾斜层状薄矿床采矿方法的优选模型,其变量涉及到技术、经济、安全和环境4类影响因子中的10种影响因子,并以江西某钒矿为例,设计了5种备选采矿方法,得出待选方案的采矿方法综合优越度依次为0.498、0.364、0.621、0.351、0.651,从而确定分层条带协同开采嗣后充填法为最优。研究结果表明,基于FIS(Mamdani型)的采矿方法优选模型简单易操作且科学可靠。

Multi-robot hunting strategy based on FIS and artificial immune algorithm

data is gathered to describe the relative location and relative motion state of the robots, which in turn forms the beginning stage of the fuzzy rule. The artificial immune algorithm optimizes and generates the rule data base and adaptive design considers factors in measuring the hunting efficiency. The optimized rules are applied to the hunting task and the results show that the algorithm can effectively actualize hunting of multiple mobile robots.

Developing a Novel Method for Road Hazardous Segment Identification Based on Fuzzy Reasoning and GIS

Roads are one of the most important infrastructures in any country. One problem on road based transportation networks is accident. Current methods to identify of high potential segments of roads for accidents are based on statistical approaches that need statistical data of accident occurrences over an extended period of time so this cannot be applied to newly-built roads. In this research a new approach for road hazardous segment identification (RHSI) is introduced using Geospatial Information System (GIS) and fuzzy reasoning. In this research among all factors that usually play critical roles in the occurrence of traffic accidents, environmental factors and roadway design are considered. Using incomplete data the consideration of uncertainty is herein investigated using fuzzy reasoning. This method is performed in part of Iran's transit roads (Kohin-Loshan) for less expensive means of analyzing the risks and road safety in Iran. Comparing the results of this approach with existing statistical methods shows advantages when data are uncertain and incomplete, specially for recently built transportation roadways where statistical data are limited. Results show in some instances accident locations are somewhat displaced from the segments of highest risk and in few sites hazardous segments are not determined using traditional statistical methods.

Power Curve Modelling for Wind Turbine Using Artificial Intelligence Tools and Pre-established Inference Criteria

We propose a new way to develop non-parametric models of power curves using artificial intelligence tools.One parametric model and eight non-parametric models are developed to emulate the behavior described by the power curve of the wind farms.A comparison between the power curve models based on artificial neural networks(ANNs)and those based on fuzzy logic are also proposed.Some of the power curve models based on ANNs and fuzzy inference systems(FISs)are used as well as two new FISs with the proposed new heuristic.An initial pre-training is proposed,resulting from the characteristics derived from the expert inference followed by a transformation of a fuzzy Mamdani system into a fuzzy Sugeno system.Although the presented values by the error indicators are comparable,the results show that the new pre-trained FIS models have better precision compared with the ANN and FIS models.The comparative study is conducted in two wind farms located in northeastern Brazil.The proposed method is a relevant alternative to improve power curve approximation based on an FIS.

Functional-type Single-input-rule-modules Connected Neural Fuzzy System for Wind Speed Prediction

Wind is one kind of clean and free renewable energy sources. Wind speed plays a pivotal role in the wind power output. However, due to the random and unstable nature of the wind, accurate prediction of wind speed is a particularly challenging task. This paper presents a novel neural fuzzy method for the hourly wind speed prediction. Firstly, a neural structure is proposed for the functional-type single-input-rule-modules(FSIRMs) connected fuzzy inference system(FIS) to combine the merits of both the FSIRMs connected FIS and the neural network. Then, in order to achieve both the smallest training errors and the smallest parameters, a least square method based parameter learning algorithm is presented for the proposed FSIRMs connected neural fuzzy system(FSIRMNFS). Further,the proposed FSIRMNFS and its parameter learning algorithm are applied to the hourly wind speed prediction. Experiments and comparisons are also made to show the effectiveness and advantages of the proposed approach. Experimental results verified that our study has presented an effective approach for the hourly wind speed prediction. The proposed approach can also be used for the prediction of wind direction, wind power and some other prediction applications in the research field of renewable energy.

A New Approach to Disk Scheduling Using Fuzzy Logic

Disk scheduling is one of the main responsibilities of Operating System. OS manages hard disk to provide best access time. All major Disk scheduling algorithms incorporate seek time as the only factor for disk scheduling. The second factor rotational delay is ignored by the existing algorithms. This research paper considers both factors, Seek Time and Rotational Delay to schedule the disk. Our algorithm Fuzzy Disk Scheduling (FDS) looks at the uncertainty associated with scheduling incorporating the two factors. Keeping in view a Fuzzy inference system using If-Then rules is designed to optimize the overall performance of disk drives. Finally we compared the FDS with the other scheduling algorithms.

Fault analysis method of integrated high voltage direct current transmission lines for onshore wind farm

Voltage source converter(VSC) based high voltage direct current(HVDC) transmission is most suited for the wind farm as it allows flexibility for reactive power control in multi-terminal transmission lines and transmits low power over smaller distance. In this work, a new method has been proposed to detect the fault, identify the section of faults and classify the pole of the fault in DC transmission lines fed from onshore wind farm. In the proposed scheme, voltage signal from rectifier end terminal is extracted with sampling frequency of 1 k Hz given as the input to the detection, classification and section discrimi-nation module. In this work, severe AC faults are also considered for section discrimination. Proposed method uses fuzzy inference system(FIS) to carry out all relaying task. The reach setting of the relay is 99.9% of the transmission line. Besides, the protection covers and discriminates the grounding fault with fault resistance up to 300 Ω.Considering the results of the proposed method, it can beused effectively in real power network.

Structure identification and IO space partitioning in a nonlinear fuzzy system for prediction of patient survival after surgery

Purpose-As far as the treatment of most complex issues in the design is concerned,approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence,particularly this involves dealing with vagueness,multi-objectivity and good amount of possible solutions.In practical applications,computational techniques have given best results and the research in this field is continuously growing.The purpose of this paper is to search for a general and effective intelligent tool for prediction of patient survival after surgery.The present study involves the construction of such intelligent computational models using different configurations,including data partitioning techniques that have been experimentally evaluated by applying them over realistic medical data set for the prediction of survival in pancreatic cancer patients.Design/methodology/approach-On the basis of the experiments and research performed over the data belonging to various fields using different intelligent tools,the authors infer that combining or integrating the qualification aspects of fuzzy inference system and quantification aspects of artificial neural network can prove an efficient and better model for prediction.The authors have constructed three soft computing-based adaptive neuro-fuzzy inference system(ANFIS)models with different configurations and data partitioning techniques with an aim to search capable predictive tools that could deal with nonlinear and complex data.After evaluating the models over three shuffles of data(training set,test set and full set),the performances were compared in order to find the best design for prediction of patient survival after surgery.The construction and implementation of models have been performed using MATLAB simulator.Findings-On applying the hybrid intelligent neuro-fuzzy models with different configurations,the authors were able to find its advantage in predicting the survival of patients with pancreatic cancer.Experimental results and comparison between the constructed models conclude that ANFIS with Fuzzy C-means(FCM)partitioning model provides better accuracy in predicting the class with lowest mean square error(MSE)value.Apart from MSE value,other evaluation measure values for FCM partitioning prove to be better than the rest of the models.Therefore,the results demonstrate that the model can be applied to other biomedicine and engineering fields dealing with different complex issues related to imprecision and uncertainty.Originality/value-The originality of paper includes framework showing two-way flow for fuzzy system construction which is further used by the authors in designing the three simulation models with different configurations,including the partitioning methods for prediction of patient survival after surgery.Several experiments were carried out using different shuffles of data to validate the parameters of the model.The performances of the models were compared using various evaluation measures such as MSE.

Motion control design for unmanned ground vehicle in dynamic environment using intelligent controller

Purpose–The motion control of unmanned ground vehicles(UGV)is a challenge in the industry of automation.The purpose of this paper is to propose a fuzzy inference system(FIS)based on sensory information for solving the navigation challenge of UGV in cluttered and dynamic environments.Design/methodology/approach–The representation of the dynamic environment is a key element for the operational field and for the testing of the robotic navigation system.If dynamic obstacles move randomly in the operation field,the navigation problem becomes more complicated due to the coordination of the elements for accurate navigation and collision-free path within the environmental representations.This paper considers the construction of the FIS,which consists of two controllers.The first controller uses three sensors based on the obstacles distances from the front,right and left.The second controller employs the angle difference between the heading of the vehicle and the targeted angle to obtain the optimal route based on the environment and reach the desired destination with minimal running power and delay.The proposed design shows an efficient navigation strategy that overcomes the current navigation challenges in dynamic environments.Findings–Experimental analyses are conducted for three different scenarios to investigate the validation and effectiveness of the introduced controllers based on the FIS.The reported simulation results are obtained using MATLAB software package.The results show that the controllers of the FIS consistently perform the manoeuvring task and manage the route plan efficiently,even in a complex environment that is populated with dynamic obstacles.The paper demonstrates that the destination was reached optimally using the shortest free route.Research limitations/implications–The paper represents efforts toward building a dynamic environment filled with dynamic obstacles that move at various speeds and directions.The methodology of designing the FIS is accomplished to guide the UGV to the desired destination while avoiding collisions with obstacles.However,the methodology is approached using two-dimensional analyses.Hence,the paper suggests several extensions and variations to develop a three-dimensional strategy for further improvement.Originality/value–This paper presents the design of a FIS and its characterizations in dynamic environments,specifically for obstacles that move at different velocities.This facilitates an improved functionality of the operation of UGV.

Well-being analysis of GSU transformer insulation incorporating the impact on power generation using fuzzy logic

With the prevailing power scenario, every watt-second of electrical energy has its own merit in satisfying the consumer demand. At the state of such a stringent energy demanding era, failure of a power generation equipment compounds the energy constraints which will not only result in a huge loss of generation but also have an impact on capital revenue. The unexpected failure of generator step-up （GSU） transformer is espe- cially a major disturbance in the power system operation and leads to unscheduled outages with power delivery problems. The time lag in bringing back the equipment in service after rectification or replacement may increase the criticality as the process involves mobilization of spares and maintenance professionals. Hot atmosphere existing in the vicinity of thermal power stations running round-the- clock with more than 100% plant load factor （PLF） increases the thermal stress of the electrical insulation which leads to premature failure of windings, bushings, core laminations, etc. The healthy state of the GSU transformer has to be ensured to minimize the loss of power generation. As the predication related to failure of a GSU transformer is associated with some uncertainties, a fuzzy approach is employed in this paper along with actual field data and case studies for the well-being analysis of GSU transformer.