Rules Of Fuzzy Inference System Research Articles

Optimization of Fuzzy Social Force Model Adaptive Parameter using Genetic Algorithm for Mobile Robot Navigation Control

The Social Force Model (SFM) is a popular navigation technique for mobile robots that is primarily used to simulate pedestrian movement. The SFM method's drawback is that several parameter values, such as gain, k, and impact range, σ, must be determined manually. The reaction of the SFM is frequently inappropriate for certain environmental circumstances as a result of this manual determination. In this paper, we propose employing the Fuzzy Inference System (FIS), whose rules are optimized using a Genetic Algorithm (GA) to manage the value of the gain, k, parameter adaptive. The relative distance, d, and relative angle, α, concerning the robot's obstacle are the inputs for the FIS. The test results using a 3-D realistic CoppeliaSim demonstrated that the learning outcomes of FIS rules could provide adaptive parameter values suitable for each environmental circumstance, allowing the robot to travel smoothly is represented using the robot’s heading deviation which decreasing by and reaching the goal 1.6 sec faster from the starting point to the goal, compared to the SFM with the fixed parameter value. So that the proposed method is more effective and promising when deploying on the real robot implementation.

Responsive Motion Control for Robot Soccer Navigation Using Adaptive Social Force Framework

This paper presents a modified Social Force Model (SFM) for navigation control of a soccer robot application. We modified the way of determining the parameter value of the gain factor, , of the SFM using the Fuzzy Inference System (FIS), so that the value of the gain factor, , is adaptive. The purpose of the gain factor adaptation is that the robot can move responsively but not over-reactive when it encounters an obstacle at high speed, which is a weakness of SFM with fixed parameters. Modification of SFM parameters using FIS is hereinafter referred to as the Fuzzy-based Social Force Model (F-SFM). We used this technique on a soccer robot with an omnidirectional drive platform with three motors. As an experiment, several modifications to the FIS rules were made and compared to the SFM with fixed parameters. The simulation-based experimental results show that the proposed method outperforms the SFM method with fixed-parameters, and the computation time does not differ significantly so that it can be applied for real implementation.

Mamdani fuzzy inference systems and artificial neural networks for landslide susceptibility mapping

Two Artificial Intelligence (AI) methods, Fuzzy Inference System (FIS) and Artificial Neural Network (ANN), are applied to Landslide Susceptibility Mapping (LSM), to compare complementary aspects of the potentials of the two methods and to extract physical relationships from data. An index is proposed in order to rank and filter the FIS rules, selecting a certain number of readable rules for further interpretation of the physical relationships among variables. The area of study is Rolante river basin, southern Brazil. Eleven attributes are generated from a Digital Elevation Model (DEM), and landslide scars from an extreme rainfall event are used. Average accuracy and area under Receiver Operating Characteristic curve (AUC) resulted, respectively, in 81.27% and 0.8886 for FIS, and 89.45% and 0.9409 for ANN. ANN provides a map with more amplitude of outputs and less area classified as high susceptibility. Among the 40 (10%) best-ranked FIS rules, 13 have high susceptibility output, while 27 have low; a cause is that low susceptibility areas are larger on the map. Slope is highly connected to susceptibility. Elevation, when high (plateau) or low (floodplain), inhibits high susceptibility. Six attributes show the same fuzzy set for the 18 best-ranked rules, meaning this fuzzy set is common on the map. Overall findings point out that ANN is best suited for LSM map generation, but, based on them, using FIS is important to help researchers understand more about AI models for LSM and about landslide phenomenon.

A New Complex Fuzzy Inference System With Fuzzy Knowledge Graph and Extensions in Decision Making

Context and Background:Complex fuzzy theory has a strong practical implication in many real-world applications. Complex Fuzzy Inference System (CFIS) is a powerful technique to overcome the challenges of uncertain, periodic data. However, a question is raised for CFIS: How can we deduce and predict the result in case there is little knowledge about data information and rule base? This is significance because many real applications do not have enough knowledge of rule base for inference so that the performance of systems may be low. Thus, it is necessary to have an approximate reasoning method to represent and derive final results. Motivation: Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been proposed with a specific inference mechanism according to the Mamdani type. A new improvement so-called the Mamdani Complex Fuzzy Inference System with Rule Reduction (M-CFIS-R) has been designed to utilize granular computing with complex similarity measures to reduce the rule base so as to gain better performance in decision-making problems. However in M-CFIS-R, testing data are checked by matching with each rule in the rule base, which leads to a high cost of computational time. Besides, if the testing data contain records that are not inferred by the rule base, the output cannot be generated. This happens in real commerce systems in which the rule base is small at the time of creation and needs to feed with new rules. Methodology: In order to handle those issues, this article first time proposes the Fuzzy Knowledge Graph to represent the rule base in terms of linguistic labels and their relationships according to the rule set. An adjacent matrix of Fuzzy Knowledge Graph is generated for inference. When a record in the Testing dataset is given, it would be fuzzified and labelled. Each component in the record is checked with the Fuzzy Knowledge Graph by the inference mechanism in approximate reasoning called Fast Inference Search Algorithm. Then, we derive the label of the new record by the Max-Min operator. Besides, we also propose four extensions of Mamdani Complex Fuzzy Inference System Rule Reduction including Sugeno Complex Fuzzy Inference Systems, Tsukamoto Complex Fuzzy Inference Systems, Complex Fuzzy Measures and Complex Fuzzy Integrals in M-CFIS-R. Results: The experiments on the UCI Machine Learning datasets show that the proposed method classifies samples as correctly as M-CFIS-R with very lower run time (6.45 times on average). The experiments are performed through many tests via 2 main scenarios. Conclusion: The proposed system has good performance in reducing the computational time of inference with acceptable accuracy. It has ability to work with systems having limited knowledge and rule base.

Decoupled Adaptive Neuro-Interval Type-2 Fuzzy Sliding Mode Control Applied in a 3DCrane System

Moving an object attached to a cable along a predetermined path is a very complex task when the angles of oscillation impose severe constraints. However, to minimize the swing angle, adaptive control laws are necessary, especially in case where the systems dynamics are prone to uncertainties. In this paper, we propose a decoupled adaptive neuro-interval type-2 fuzzy controller based on the sliding mode theory for the control of 3DCrane system. The considered 3DCrane system involves a plan movement in conjunction with a lifting movement. It has three control inputs only (trolley and hoisting forces) with five controlled variables (the trolley position in the XOY plane, the length of the lifting cable, and the two angles of swing). Overall, control subsystems are regarded as being decoupled interactions and that are taken as disturbances acting in the control of each individual subsystem. In the proposed approach, a conventional controller (PD) and the neuro-interval type-2 fuzzy controller are used in parallel; the PD controller ensures the asymptotic stability in compact space, the adaptation parameters of neuro-interval type-2 fuzzy inference system rules are obtained by derivation, and Lyapunov method is used to demonstrate the stability of the online learning algorithm. To validate the proposed approach, the laboratory equipment 3DCrane system is used as an experimental platform. The presented results are obtained for a trajectory tracking with a minimum of oscillations.

Future Weather Forecasting Using Soft Computing Techniques

Prediction of weather is determination of future weather condition based on different weather parameters. The different weather parameters were recorded day to day wise. In “Future Weather Forecasting using Soft Computing Technique” the neural network is trained using different combination of weather parameters, the parameter used are humidity, temperature, pressure, wind speed, dew point and visibility. After training the neural network all predicted values of humidity, temperature, pressure, wind speed, dew point and visibility are applied the fuzzy inference system, in fuzzy inference system rules were created. Now applying all predicted parameters to fuzzy system the prediction about the future weather is done.

A hybrid intelligent approach based on computer vision and fuzzy logic for quality measurement of milled rice

In this research, a fuzzy inference system (FIS) coupled with image processing technique was developed as a decision-support system for qualitative grading of milled rice. Two quality indices, namely degree of milling (DOM) and percentage of broken kernels (PBK) were first graded by rice processing experts into five classes. Then, images of the same samples were captured using a machine vision system. The information obtained from the sample image processing was transferred to FIS. The FIS classifier consisted of two input linguistic variables, namely, DOM and PBK, and one output variable (Quality), all in the form of triangle membership functions. Altogether, 25 rules were considered in the FIS rule base using the AND operator and Mamdani inference system. In order to evaluate the developed system, statistical performance of the FIS classifier was compared with the experts’ judgments. Results of analysis showed a 89.8% agreement between the grading results obtained from the developed system and those determined by the experts.

Experimental design in soap manufacturing for optimization of fuzzified process capability index

In manufacturing industries, the quality of a product depends on the combined effect of multiple input variables working singly or together and therefore attention has been given on process capability indices to shift from single to multivariate domain. In case of multivariable domain the capability to incorporate uncertainties at the time of decision making becomes necessary. Fuzzy system is introduced to take care of this requirement. In this article the process parameters of soap manufacturing industries have been analyzed. The process capability is determined using Fuzzy Inference System rule editor based on a set of justified if then statements as applicable for the process. The data has been collected in linguistic form to derive its process capability, using a set of justified rules and the effect of each factor has been determined using Design of Experiments (DoE) and analysis of variance technique (ANOVA) for improving the soap quality from perspective of its softness. This article ventures to propose a new methodology by integrating Fuzzy with DoE providing better result followed by DoE and Fuzzy Inference system in isolation.

Evaluation of short-term geomorphic changes in differently impacted gravel-bed rivers using improved dems of difference

The evaluation of the morphological dynamics of rivers is increasingly focusing, in recent years, on the achievement of quantitative estimates of change in order to identify geomorphic trends and forecast targeted restoration actions. Thanks to the development of more effective and reliable survey technologies, more accurate Digital Elevation Models (DEM) can be produced and, through their consequent differencing (DoD), extremely useful geomorphic analyses can be carried out. In this situation, a major role is played by uncertainty, especially in the final volumetric rates of erosion and deposition processes, that may lead to misinterpretation of spatial and temporal changes. This paper aims at achieving precise geomorphic estimates derived from subsequent hybrid (LiDAR and bathymetric points) surface representations. The study areas consist of gravel-bed reaches of two differently impacted fluvial environments, Piave and Tagliamento rivers, that were affected by two severe flood events (Piave, R.I. of 7 and 10 years and Tagliamento, R.I. of 15 and 12 years) in the inter-surveys period. The basic Hybrid Digital Elevation Models (HDTM) were processed accounting for spatially variable uncertainty and considering, beside slope and point density input variables, a novel component measuring the quality of the bathymetric derived points. In fact, since the major changes occur within river channels, the integration of this variable evaluating the precision of the bathymetric channel elevations in the HDTMs, has allowed, through the creation of targeted FIS (Fuzzy Inference System) rules, to obtain reliable geomorphic estimates of change. Volumes and erosion and deposition patterns were then analyzed and compared to outline the different dynamics among the sub-reaches and the two river systems.

Evaluation of short-term geomorphic changes in differently impacted gravel-bed rivers using improved dems of difference

The evaluation of the morphological dynamics of rivers is increasingly focusing, in recent years, on the achievement of quantitative estimates of change in order to identify geomorphic trends and forecast targeted restoration actions. Thanks to the development of more effective and reliable survey technologies, more accurate Digital Elevation Models (DEM) can be produced and, through their consequent differencing (DoD), extremely useful geomorphic analyses can be carried out. In this situation, a major role is played by uncertainty, especially in the final volumetric rates of erosion and deposition processes, that may lead to misinterpretation of spatial and temporal changes. This paper aims at achieving precise geomorphic estimates derived from subsequent hybrid (LiDAR and bathymetric points) surface representations. The study areas consist of gravel-bed reaches of two differently impacted fluvial environments, Piave and Tagliamento rivers, that were affected by two severe flood events (Piave, R.I. of 7 and 10 years and Tagliamento, R.I. of 15 and 12 years) in the inter-surveys period. The basic Hybrid Digital Elevation Models (HDTM) were processed accounting for spatially variable uncertainty and considering, beside slope and point density input variables, a novel component measuring the quality of the bathymetric derived points. In fact, since the major changes occur within river channels, the integration of this variable evaluating the precision of the bathymetric channel elevations in the HDTMs, has allowed, through the creation of targeted FIS (Fuzzy Inference System) rules, to obtain reliable geomorphic estimates of change. Volumes and erosion and deposition patterns were then analyzed and compared to outline the different dynamics among the sub-reaches and the two river systems.

A Fuzzy Logic Approach for Pavement Section Classification

Section classification is one of the primary challenges in any successful pavement management system. Sections are normally classified based on their pavement condition index in order to categorise as ”good”, ”moderate” and ”poor”. Conventionally, this has been done by comparing various pavement distress data against threshold values. However, borderline values between two categories have significant influence on the subsequent pavement maintenance and rehabilitation decision. This study is the first attempt to create a system based on fuzzy logic to estimate the pavement condition index (PCI). In this paper, section data classifications are conducted using a fuzzy inference system (FIS) to utilise multiple distress data such as cracking, patching, bleeding and ravelling to develop a membership function for each defect. A FIS rule based system was then used to develop a fuzzified pavement condition index (PCI) for section classification. The result showed good agreement with the conventional PCI based pavement classification system. The proposed system has the potential to realistically differentiate pavement sections, which would aid to have economical maintenance and rehabilitation decision.

Instance-specific multi-objective parameter tuning based on fuzzy logic

Finding good parameter values for meta-heuristics is known as the parameter setting problem. A new parameter tuning strategy, called IPTS, is proposed that is a novel instance-specific method to take the trade-off between solution quality and computational time into consideration. Two important steps in the method are an a priori statistical analysis to identify the factors that determine heuristic performance in both quality and time for a specific type of problem, and the transformation of these insights into a fuzzy inference system rule base which aims to return parameter values on the Pareto-front with respect to a decision maker’s preference. Applied to the symmetric Travelling Salesman Problem and the meta-heuristic Guided Local Search, the approach is consistently faster than a traditional non-instance-specific parameter tuning strategy without significantly affecting solution quality; optimised for speed, computational times are shown to be on average 20 times faster while producing solutions of similar quality. A number of interesting areas for further research are discussed.

The Muskingum flood routing model using a neuro-fuzzy approach

The study presents the combined application of Fuzzy Inference System (FIS) and Muskingum model in flood routing. The rules of FIS are incorporated with the Muskingum formula and the model is called the Muskingum FIS model in the study. The proposed model estimates the outflow by applying a Network-based Fuzzy Inference System (ANFIS), which is a FIS implemented in the adaptive network framework. Simulation results indicate that the proposed scheme is an advisable approach for the flood routing. Case study is presented to demonstrate that the FIS is an alternative in application of the Muskingum model.

OPTIMIZATION OF FUZZY INFERENCE RULES BY USING THE GENETIC ALGORITHM AND ITS APPLICATION TO THE BOND RATING

This paper deals with the optimization of membership function in the rules of fuzzy inference system by using the Genetic Algorithm, and also shows its application to the classification of corporate bond. In the fuzzy inference system the parameters such as the weight are determined to minimize the difference between the prescribed value and the output of the system. However, the output of the system also depends on the shape of the membership function. We utilize the Genetic Algorithm to select better shape of the membership function. The method is applied to the automatic classification of corporate bond (so called bond rating) of Japanese firms. The result shows about 5% improvement of the bond rating compared to the conventional fuzzy inference system.