A Cascaded Fuzzy Inference System for Indian river water quality prediction

Now-a-days, Fuzzy Inference System (FIS) is considered as an effective tool for solution of many complex engineering systems when ambiguity and uncertainly is associated with the systems. Mamdani and Takagi, Sugeno and Kang (TSK) models poses simplicity in modeling but their system performance prediction capability is severely affected as complexity of the problem increases. In a multi-input, multi-output situation where a system consists of many subsystems and different outputs are desired from each subsystem, an improved version of FIS must be adopted rather than developing FIS for each subsystem. When dealing with such a system, it is prudent to use cascading systems rather than developing models for individual systems. To this end, a new Cascaded Mamdani Fuzzy Inference System is proposed in this paper and its performance is evaluated with the help of prediction of Indian River water quality index (WQI). In general, WQI value is a dimensionless number ranging from 0 to 100 (best quality) and plays an important role in evaluating the water quality of rivers. The proposed model is designed to predict WQI for five rivers in India. The cascaded fuzzy system simplifies and speeds up the computation of WQI as compared to the currently existing standards. In this paper, the proposed model is compared with three International water quality criteria and it is found that the designed model results in accurate prediction.     

Interval type-2 fuzzy expert system for prediction of carbon monoxide concentration in mega-cities

This paper presents an indirect approach to interval type-2 fuzzy logic system modeling to forecaste the level of air pollutants. The type-2 fuzzy logic system permits us to model the uncertainties among rules and the parameters related to data analysis. In this paper, we propose an indirect method to create an interval type-2 fuzzy logic system from a historical data, where Footprint of Uncertainties of fuzzy sets are extracted by implementation of an interval type-2 FCM algorithm and based on an upper and lower value for the level of fuzziness m in FCM. Finally, the proposed model is applied for prediction of carbon monoxide concentration in Tehran air pollution. It is shown that the proposed type-2 fuzzy logic system is superior in comparison to type-1 fuzzy logic systems in terms of two performance indices.     

Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and CO2 flux measurements in rice

We explored simple and useful spectral indices for estimating photosynthetic variables (radiation use efficiency and photosynthetic capacity) at a canopy scale based on seasonal measurements of hyperspectral reflectance, ecosystem CO₂ flux, and plant and micrometeorological variables. An experimental study was conducted over the simple and homogenous ecosystem of an irrigated rice field. Photosynthetically active radiation absorbed by the canopy (APAR), the canopy absorptivity of APAR (fAPAR), net ecosystem exchange of CO₂ (NEE_CO2) gross primary productivity (GPP), photosynthetic capacity at the saturating APAR (P_max), and three parameters of radiation use efficiency (ε_N: NEE_CO2/APAR; ε_G: GPP/APAR; φ: quantum efficiency) were derived from the data set. Based on the statistical analysis of relationships between these ecophysiological variables and reflectance indicators such as normalized difference spectral indices (NDSI[i,j]) using all combinations of two wavelengths (i and j nm), we found several new indices that would were more effective than conventional spectral indices such as photochemical reflectance index (PRI) and normalized difference vegetation index (NDVI = NDSI[near-infrared, red]). ε_G was correlated well with NDSI[710, 410], NDSI[710, 520], and NDSI[530, 550] derived from nadir measurements. φ was best correlated with NDSI[450, 1330]. NDSI[550, 410] and NDSI[720, 420] had a consistent linear relationships with fAPAR throughout the growing season, whereas conventional indices such as NDVI showed very different relationships before and after heading. Off-nadir measurements were more closely related to the efficiency parameters than nadir measurements. Our results provide useful insights for assessing plant productivity and ecosystem CO₂ exchange, using a wide range of available spectral data as well as useful information for designing future sensors for ecosystem observations.     

Implementation of a microcontroller-based simplified FITA-FIS model

The paper describes basic approach to building a general purpose MISO-FITA (multiple inputs single output rule based system) fuzzy logic inference system. It is also discussed classic and simplified models of the inference systems and some optimization methods of its architecture. The fuzzy engine of the proposed system is based on simplified Mamdani’s fuzzy inference model. It has been implemented on the sample platform based on ARMv7 Cortex-M4 microcontroller. The performance of the fuzzy inference system, defined as a time to obtain an output crisp inference result, is higher or comparable to another software and hardware solutions. For proposed system it even takes 10 μs.     

Assessment and improvement of quality of service in wireless networks using fuzzy and hybrid genetic-fuzzy approaches

Fuzzy and hybrid genetic-fuzzy approaches were used to assess and improve quality of service (QoS) in simulated wireless networks. Three real-time audio and video applications were transmitted over the networks. The QoS provided by the networks for each application was quantitatively assessed using a fuzzy inference system (FIS). Two methods to improve the networks’ QoS were developed. One method was based on a FIS mechanism and the other used a hybrid genetic-fuzzy system. Both methods determined an optimised value for the minimum contention window (CW _min) in IEEE 802.11 medium access control (MAC) protocol. CW _min affects the time period a wireless station waits before it transmits a packet and thus its value influences QoS. The average QoS for the audio and video applications improved by 42.8% and 14.5% respectively by using the FIS method. The hybrid genetic-fuzzy system improved the average QoS for the audio and video applications by 35.7% and 16.5% respectively. The study indicated that the devised methods were effective in assessing and significantly improving QoS in wireless networks.     

Controlling a drone: Comparison between a based model method and a fuzzy inference system

The work describes an automatically on-line self-tunable fuzzy inference system (STFIS) of a new configuration of mini-flying called XSF (X4 Stationnary Flyer) drone. A fuzzy controller based on on-line optimization of a zero order Takagi–Sugeno fuzzy inference system (FIS) by a back propagation-like algorithm is successfully applied. It is used to minimize a cost function that is made up of a quadratic error term and a weight decay term that prevents an excessive growth of parameters. Thus, we carried out control for the continuation of simple trajectories such as the follow-up of straight lines, and complex (half circle, corner, and helicoidal) by using the STFIS technique. This permits to prove the effectiveness of the proposed control law. Simulation results and a comparison with a static feedback linearization controller (SFL) are presented and discussed. We studied the robustness of the two controllers used in the presence of disturbances. We presented two types of disturbances, the case of a breakdown of an engine as well as a gust of wind.     

Self-learning fuzzy logic controllers for pursuit-evasion differential games

This paper addresses the problem of tuning the input and the output parameters of a fuzzy logic controller. The system learns autonomously without supervision or a priori training data. Two novel techniques are proposed. The first technique combines Q(λ)-learning with function approximation (fuzzy inference system) to tune the parameters of a fuzzy logic controller operating in continuous state and action spaces. The second technique combines Q(λ)-learning with genetic algorithms to tune the parameters of a fuzzy logic controller in the discrete state and action spaces. The proposed techniques are applied to different pursuit-evasion differential games. The proposed techniques are compared with the classical control strategy, Q(λ)-learning only, reward-based genetic algorithms learning, and with the technique proposed by Dai et al. (2005) [19] in which a neural network is used as a function approximation for Q-learning. Computer simulations show the usefulness of the proposed techniques.     

Systemic approach to fuzzy logic formalization for approximate reasoning

L.A. Zadeh, E.H. Mamdani, M. Mizumoto, et al., R.A. Aliev and A. Tserkovny have proposed methods for fuzzy reasoning in which antecedents and consequents involve fuzzy conditional propositions of the form “If x is A then y is B”, with A and B being fuzzy concepts (fuzzy sets). A formulation of fuzzy antecedent/consequent chains is one of the most important topics within a wide spectrum of problems in fuzzy sets in general and approximate reasoning, in particular. From the analysis of relevant research it becomes clear that for this purpose, a so-called fuzzy conditional inference rules comes as a viable alternative. In this study, we present a systemic approach toward fuzzy logic formalization for approximate reasoning. For this reason, we put together some comparative analysis of fuzzy reasoning methods in which antecedents contain a conditional proposition with fuzzy concepts and which are based on implication operators present in various types of fuzzy logic. We also show a process of a formation of the fuzzy logic regarded as an algebraic system closed under all its operations. We examine statistical characteristics of the proposed fuzzy logic. As the matter of practical interest, we construct a set of fuzzy conditional inference rules on the basis of the proposed fuzzy logic. Continuity and stability features of the formalized rules are investigated.     

Estimating the uniaxial compressive strength of some clay-bearing rocks selected from Turkey by nonlinear multivariable regression and rule-based fuzzy models

Abstract: Although the use of predictive models in rock engineering and engineering geology is an important issue, some simple and multivariate regression techniques traditionally employed in these areas have recently been challenged by the use of fuzzy inference systems and artificial neural networks. The purpose of this study was to construct some predictive models to estimate the uniaxial compressive strength of some clay-bearing rocks, depending on examination of their slake durability indices and clay contents. For this purpose, the simple and nonlinear multivariable regression techniques and the Mamdani fuzzy algorithm are compared in terms of their accuracy. To increase the accuracy of the Mamdani fuzzy inference system, the weighted if–then rules are extracted. To compare the predictive performances of the models, the statistical performance indices (root mean square error and variance account for) are calculated and the results are discussed. The indices reveal that the fuzzy inference system has a slightly higher prediction capacity than the regression models. The basic reason for the higher performance of the fuzzy inference system is the flexibility of the fuzzy approach.     

Air quality prediction by neuro-fuzzy modeling approach

This paper proposes an air quality prediction system based on the neuro-fuzzy network approach. Historical time series data are employed to derive a set of fuzzy rules, or equivalently a neuro-fuzzy network, for forecasting air pollutant concentrations and environmental factors in the future. Due to the uncertainty of the involved impact factors, fuzzy elements are added to the forecasting system. First of all, training data are partitioned into fuzzy clusters whose membership functions are characterized by the estimated means and variances. From these fuzzy clusters, fuzzy rules are extracted and a four-layer fuzzy neural network is constructed. Then genetic, particle swarm optimization, and steepest descent backpropagation algorithms are applied to train the network. The network outputs, derived through the fuzzy inference process, produce the forecast air pollutant concentrations or air quality indices. Our proposed approach has the following advantages: (1) Adding fuzzy elements can more appropriately deal with the uncertainty of the impact factors involved; (2) The distribution of training data can be described properly by fuzzy clusters with statistical means and variances; (3) Fuzzy rules are extracted automatically from the training data, instead of being supplied manually by human experts; (4) The obtained fuzzy rules are of high quality, and their parameters can be optimized effectively.     

Fuzzy conditional inference under max-⊙ composition

This paper shows that the majority of fuzzy inference methods for a fuzzy conditional proposition “If x is A then y is B,” with A and B fuzzy concepts, can infer very reasonable consequences which fit our intuition with respect to several criteria such as modus ponens and modus tollens, if a new composition called “max-⊙ composition” is used in the compositional rule of inference, though reasonable consequences cannot always be obtained when using the max-min composition, which is used usually in the compositional rule of inference. Furthermore, it is shown that a syllogism holds for the majority of the methods under the max-⊙ composition, though they do not always satisfy the syllogism under the max-min composition.     

Approximation of fuzzy context-free grammars

Fuzzy context-free max- grammar (or FCFG, for short), as a straightforward extension of context-free grammar, has been introduced to express uncertainty, imprecision, and vagueness in natural language fragments. Li recently proposed the approximation of fuzzy finite automata, which may effectively deal with the practical problems of fuzziness, impreciseness and vagueness. In this paper, we further develop the approximation of fuzzy context-free grammars. In particular, we show that a fuzzy context-free grammar under max- compositional inference can be approximated by some fuzzy context-free grammar under max-min compositional inference with any given accuracy. In addition, some related properties of fuzzy context-free grammars and fuzzy languages generated by them are studied. Finally, the sensitivity of fuzzy context-free grammars is also discussed.     

Process capability analyses based on fuzzy measurements and fuzzy control charts

Process performance can be analyzed by using process capability indices (PCIs), which are summary statistics to depict the process location and dispersion successfully. Although they are very usable statistics, they have some limitations which prevent a deep and flexible analysis because of the crisp measurements and specification limits (SLs). If the specification limits or measurements are expressed by linguistic variables, traditional PCIs cause some misleading results. In this paper, the fuzzy set theory is used to add more information and flexibility to process capability analyses (PCA). For this aim, linguistic definition of the quality characteristic measurements are converted to fuzzy numbers and fuzzy PCIs are produced based on these measurements and fuzzy specification limits (SLs). Also fuzzy control charts are derived for fuzzy measurements of the related quality characteristic. They are used to increase the accuracy of PCA by determining whether or not the process is in statistical control. The fuzzy formulation of the indices C_p and C_pk, which are the most used two traditional PCIs, are produced when SLs and measurements are both triangular (TFN) and trapezoidal fuzzy numbers (TrFN). The proposed methodologies are applied in a piston manufacturer in Konya’s Industrial Area, Turkey.     

Intelligent complementary sliding-mode control with dead-zone parameter modification

This paper proposes an intelligent complementary sliding-mode control (ICSMC) system which is composed of a computed controller and a robust controller. The computed controller includes a neural dynamics estimator and the robust compensator is designed to prove a finite L₂-gain property. The neural dynamics estimator uses a recurrent neural fuzzy inference network (RNFIN) to approximate the unknown system term in the sense of the Lyapunov function. In traditional neural network learning process, an over-trained neural network would force the parameters to drift and the system may become unstable eventually. To resolve this problem, a dead-zone parameter modification is proposed for the parameter tuning process to stop when tracking performance index is smaller than performance threshold. To investigate the capabilities of the proposed ICSMC approach, the ICSMC system is applied to a one-link robotic manipulator and a DC motor driver. The simulation and experimental results show that favorable control performance can be achieved in the sense of the L₂-gain robust control approach by the proposed ICSMC scheme.     

A fuzzy system to support the configuration of baggage screening devices at an airport

An airport is a complex engineering system; it is composed of many elements interconnected with numerous internal relations with a strongly pronounced role of the human factor. One of the specific tasks carried out by the airport managing entity (AME) is to configure the airport security system (ApSS) so that to attain the expected level of confidence in the airport safety and security. This task consists in selection of infrastructure, technical equipment, allocation of personnel and financial means that are necessary to perform all functions of the ApSS. One of the aspects of the configuration of the ApSS is the allocation of available X-ray baggage screening devices searching for items prohibited for transportation. To make this allocation, we need to know how effective these devices are (in terms of detecting prohibited items). This assessment is dependent on several factors which are treated as linguistic variables and are input to fuzzy inference system: the ability to detect explosives, the number of detection lines, the effectiveness of the TIP (Threat Image Projection) system and the age of the machine. Some of these elements are difficult to objective assessment, as they are heavily dependent on the human factor or the information is uncertain or incomplete. So fuzzy ApSS analysis is proposed. The output from the fuzzy inference system is linguistic variable Device evaluation. The meaning of this variable is the ability to protect the aircraft against prohibited items. The proposed new method of assessing the airport baggage screening system involves the construction of a hierarchical fuzzy inference system. The usefulness of the method is exemplified for Katowice–Pyrzowice International Airport, for which an assessment of devices has been performed. The results show that not only allocation of specific devices for specific control points is important for the security of passengers. Also important are the locally accepted principles of their work, which so far are not specified by international regulations. This applies for instance to the selection of the number and frequency of TIP images. Experiments show that the proposed approach can be effective as part of an expert system for supporting the airport operator in configuring ApSS.     

Hypoglycaemia detection using fuzzy inference system with intelligent optimiser

Hypoglycaemia is a medical term for a body state with a low level of blood glucose. It is a common and serious side effect of insulin therapy in patients with diabetes. In this paper, we propose a system model to measure physiological parameters continuously to provide hypoglycaemia detection for Type 1 diabetes mellitus (TIDM) patients. The resulting model is a fuzzy inference system (FIS). The heart rate (HR), corrected QT interval of the electrocardiogram (ECG) signal (QT_c), change of HR, and change of QT_c are used as the input of the FIS to detect the hypoglycaemic episodes. An intelligent optimiser is designed to optimise the FIS parameters that govern the membership functions and the fuzzy rules. The intelligent optimiser has an implementation framework that incorporates two wavelet mutated differential evolution optimisers to enhance the training performance. A multi-objective optimisation approach is used to perform the training of the FIS in order to meet the medical standards on sensitivity and specificity. Experiments with real data of 16 children (569 data points) with TIDM are studied in this paper. The data are randomly separated into a training set with 5 patients (l99 data points), a validation set with 5 patients (177 data points) and a testing set with 5 patients (193 data points). Experiment results show that the proposed FIS tuned by the proposed intelligent optimiser can offer good performance of classification.     

The extension of fuzzy QFD: From product planning to part deployment

By focusing on listening to the customers, quality function deployment (QFD) has been a successful analysis tool in product design and development. To solve the uncertainty or imprecision in QFD, numerous researchers have attempted to apply the fuzzy set theory to QFD and have developed various fuzzy QFD approaches. Their models usually concentrate on product planning, the first phase of QFD. The subsequent phases (part deployment, process planning, and production planning) of QFD are seldom addressed. Moreover, their models often use algebraic operations of fuzzy numbers to calculate the fuzzy sets in QFD. Biased results are easily produced after several multiplicative or divisional operations. Aiming to solve these two issues, the objective of this study is to develop an extended fuzzy quality function deployment approach (E-QFD) which expands the research scope, from product planning to part deployment. In product planning, a more advanced method for collecting customer requirements is developed while the competitive analysis is also considered. In part deployment, the original part deployment table is enhanced by including the importance of part characteristics (PCs) and the bottleneck level of PCs. A modified fuzzy k-means clustering method is proposed to classify various bottleneck (or importance) groups of PCs. The failure mode and effects analysis (FMEA) is conducted for the high bottleneck (or high importance) group of PCs through the fuzzy inference approach. Moreover, E-QFD employs a more precise method, α-cut operations, to calculate the fuzzy sets in QFD instead of algebraic operations of fuzzy numbers. Finally, a case study is given to explain the analysis process of the proposed method.     

Improved fault detection employing hybrid memetic fuzzy modeling and adaptive filters

We propose an improved fault detection (FD) scheme based on residual signals extracted on-line from system models identified from high-dimensional measurement data recorded in multi-sensor networks. The system models are designed for an all-coverage approach and comprise linear and non-linear approximation functions representing the interrelations and dependencies among the measurement variables. The residuals obtained by comparing observed versus predicted values (i.e., the predictions achieved by the system models) are normalized subject to the uncertainty of the models and are supervised by an incrementally adaptive statistical tolerance band. Upon violation of this tolerance band, a fault alarm is triggered. The improved FD methods comes with two the main novelty aspects: (1) the development of an enhanced optimization scheme for fuzzy systems training which builds upon the SparseFIS (Sparse Fuzzy Inference Systems) approach and enhances it by embedding genetic operators for escaping local minima ⟶ a hybrid memetic (sparse) fuzzy modeling approach, termed as GenSparseFIS. (2) The design and application of adaptive filters on the residual signals, over time, in a sliding-window based incremental/decremental manner to smoothen the signals and to reduce the false positive rates. This gives us the freedom to tighten the tolerance band and thus to increase fault detection rates by holding the same level of false positives. In the results section, we verify that this increase is statistically significant in the case of adaptive filters when applying the proposed concepts onto four real-world scenarios (three different ones from rolling mills, one from engine test benches). The hybridization of sparse fuzzy inference systems with genetic algorithms led to the generation of more high quality models that can in turn be used in the FD process as residual generators. The new hybrid sparse memetic modeling approach also achieved fuzzy systems leading to higher fault detection rates for some scenarios.     

Several non-linear models in estimating air-overpressure resulting from mine blasting

This research presents several non-linear models including empirical, artificial neural network (ANN), fuzzy system and adaptive neuro-fuzzy inference system (ANFIS) to estimate air-overpressure (AOp) resulting from mine blasting. For this purpose, Miduk copper mine, Iran was investigated and results of 77 blasting works were recorded to be utilized for AOp prediction. In the modeling procedure of this study, results of distance from the blast-face and maximum charge per delay were considered as predictors. After constructing the non-linear models, several performance prediction indices, i.e. root mean squared error (RMSE), variance account for (VAF), and coefficient of determination (R ²) and total ranking method are examined to choose the best predictive models and evaluation of the obtained results. It is obtained that the ANFIS model is superior to other utilized techniques in terms of R ², RMSE, VAF and ranking herein. As an example, RMSE values of 5.628, 3.937, 3.619 and 2.329 were obtained for testing datasets of empirical, ANN, fuzzy and ANFIS models, respectively, which indicate higher performance capacity of the ANFIS technique to estimate AOp compared to other implemented methods.     

Optimization of type-2 fuzzy weights in backpropagation learning for neural networks using GAs and PSO

In this paper the optimization of type-2 fuzzy inference systems using genetic algorithms (GAs) and particle swarm optimization (PSO) is presented. The optimized type-2 fuzzy inference systems are used to estimate the type-2 fuzzy weights of backpropagation neural networks. Simulation results and a comparative study among neural networks with type-2 fuzzy weights without optimization of the type-2 fuzzy inference systems, neural networks with optimized type-2 fuzzy weights using genetic algorithms, and neural networks with optimized type-2 fuzzy weights using particle swarm optimization are presented to illustrate the advantages of the bio-inspired methods. The comparative study is based on a benchmark case of prediction, which is the Mackey-Glass time series (for τ = 17) problem.