Showing papers on "Neuro-fuzzy published in 2017"
TL;DR: The prediction performances of EFNN are better than those of traditional models due to their strong learning ability and as the prediction time step increases, the EFNN model can consider the periodic pattern and demonstrate advantages over other models with smaller predicted errors and slow raising rate of errors.
Abstract: This paper proposes a new method in construction fuzzy neural network to forecast travel speed for multi-step ahead based on 2-min travel speed data collected from three remote traffic microwave sensors located on a southbound segment of a fourth ring road in Beijing City. The first-order Takagi–Sugeno system is used to complete the fuzzy inference. To train the evolving fuzzy neural network (EFNN), two learning processes are proposed. First, a $K$ -means method is employed to partition input samples into different clusters and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated. Second, a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi–Sugeno type fuzzy rules. Furthermore, a trigonometric regression function is introduced to capture the periodic component in the raw speed data. Specifically, the predicted performance between the proposed model and six traditional models are compared, which are artificial neural network, support vector machine, autoregressive integrated moving average model, and vector autoregressive model. The results suggest that the prediction performances of EFNN are better than those of traditional models due to their strong learning ability. As the prediction time step increases, the EFNN model can consider the periodic pattern and demonstrate advantages over other models with smaller predicted errors and slow raising rate of errors.
316 citations
TL;DR: It is shown how to introduce the concepts of fuzzy learning into DL to overcome the shortcomings of fixed representation and the fuzzy dDL paradigm greatly outperforms other nonfuzzy and shallow learning approaches on these tasks.
Abstract: Deep learning (DL) is an emerging and powerful paradigm that allows large-scale task-driven feature learning from big data. However, typical DL is a fully deterministic model that sheds no light on data uncertainty reductions. In this paper, we show how to introduce the concepts of fuzzy learning into DL to overcome the shortcomings of fixed representation. The bulk of the proposed fuzzy system is a hierarchical deep neural network that derives information from both fuzzy and neural representations. Then, the knowledge learnt from these two respective views are fused altogether forming the final data representation to be classified. The effectiveness of the model is verified on three practical tasks of image categorization, high-frequency financial data prediction and brain MRI segmentation that all contain high level of uncertainties in the raw data. The fuzzy dDL paradigm greatly outperforms other nonfuzzy and shallow learning approaches on these tasks.
268 citations
TL;DR: This paper addresses the problem of an event-triggered non-parallel distribution compensation (PDC) control for networked Takagi–Sugeno (T–S) fuzzy systems, under consideration of the limited data transmission bandwidth and the imperfect premise matching membership functions.
Abstract: This paper addresses the problem of an event-triggered non-parallel distribution compensation (PDC) control for networked Takagi–Sugeno (T–S) fuzzy systems, under consideration of the limited data transmission bandwidth and the imperfect premise matching membership functions. First, a unified event-triggered T–S fuzzy model is provided, in which: 1) a fuzzy observer with the imperfect premise matching is constructed to estimate the unmeasurable states of the studied system; 2) a fuzzy controller is designed following the same premise as the observer; and 3) an output-based event-triggering transmission scheme is designed to economize the restricted network resources. Different from the traditional PDC method, the synchronous premise between the fuzzy observer and the T–S fuzzy system are no longer needed in this paper. Second, by use of Lyapunov theory, a stability criterion and a stabilization condition are obtained for ensuring asymptotically stable of the studied system. On account of the imperfect premise matching conditions are well considered in the derivation of the above criteria, less conservation can be expected to enhance the design flexibility. Compared with some existing emulation-based methods, the controller gains are no longer required to be known a priori . Finally, the availability of proposed non-PDC design scheme is illustrated by the backing-up control of a truck-trailer system.
253 citations
TL;DR: A novel approach is introduced to tackle unknown functions with nonstrict-feedback structure in the design process, and by introducing an auxiliary system, the input saturation problem can be solved and a novel adaptive fuzzy tracking controller is designed.
Abstract: This paper studies an adaptive fuzzy tracking control problem for nonlinear stochastic systems with input saturation and nonstrict-feedback form. We use fuzzy logic systems to approximate unknown nonlinear functions. A novel approach is introduced to tackle unknown functions with nonstrict-feedback structure in the design process. By introducing an auxiliary system, the input saturation problem can be solved. Moreover, based on backstepping control design approach, a novel adaptive fuzzy tracking controller is designed to guarantee all signals in the closed-loop system to be bounded, and the system output can be driven to track the trajectory of a given reference signal. Finally, some simulation results are given to confirm the effectiveness of the proposed approach.
202 citations
TL;DR: A novel fuzzy adaptive output-constrained FTC approach is constructed and all the signals in the closed-loop system are proved to be bounded in probability and the system outputs are constrained in a given compact set.
Abstract: The problem of adaptive fuzzy output-constrained tracking fault-tolerant control (FTC) is investigated for the large-scale stochastic nonlinear systems of pure-feedback form. The nonlinear systems considered in this paper possess the unstructured uncertainties, unknown interconnected terms and unknown nonaffine nonlinear faults. The fuzzy logic systems are employed to identify the unknown lumped nonlinear functions so that the problems of structured uncertainties can be solved. An adaptive fuzzy state observer is designed to solve the nonmeasurable state problem. By combining the barrier Lyapunov function theory, adaptive decentralized and stochastic control principles, a novel fuzzy adaptive output-constrained FTC approach is constructed. All the signals in the closed-loop system are proved to be bounded in probability and the system outputs are constrained in a given compact set. Finally, the applicability of the proposed controller is well carried out by a simulation example.
148 citations
TL;DR: This work considers the use of these types of orthopair fuzzy sets as a basis for the system of approximate reasoning introduced by Zadeh, referred to as OPAR, and looks at the formulation of the ideas of possibility and certainty using these orthop air fuzzy sets.
141 citations
TL;DR: An adaptive fuzzy output constrained control design approach is addressed for multi-input multioutput uncertain stochastic nonlinear systems in nonstrict-feedback form.
Abstract: In this paper, an adaptive fuzzy output constrained control design approach is addressed for multi-input multioutput uncertain stochastic nonlinear systems in nonstrict-feedback form. The nonlinear systems addressed in this paper possess unstructured uncertainties, unknown gain functions and unknown stochastic disturbances. Fuzzy logic systems are utilized to tackle the problem of unknown nonlinear uncertainties. The barrier Lyapunov function technique is employed to solve the output constrained problem. In the framework of backstepping design, an adaptive fuzzy control design scheme is constructed. All the signals in the closed-loop system are proved to be bounded in probability and the system outputs are constrained in a given compact set. Finally, the applicability of the proposed controller is well carried out by a simulation example.
137 citations
TL;DR: Simulations over 10 heterogeneous wireless sensor networks show that LEACH-SF outperforms the existing cluster-based routing protocols and can be adaptively tuned via ABC for any application.
130 citations
TL;DR: Simulation results illustrate that the implementation of the GT2FLS approach improves its performance when using the BCO algorithm and the stability of the fuzzy controller is better when compared with respect to a type-1 Fuzzy Logic Controller (T1FLC) and an Interval type-2 fuzzy logic Controller (IT2FLC).
126 citations
TL;DR: The eT2RFNN adopts a holistic concept of evolving systems, where the fuzzy rule can be automatically generated, pruned, merged, and recalled in the single-pass learning mode, and is capable of coping with the problem of high dimensionality because it is equipped with online feature selection technology.
Abstract: The age of online data stream and dynamic environments results in the increasing demand of advanced machine learning techniques to deal with concept drifts in large data streams. Evolving fuzzy systems (EFS) are one of recent initiatives from the fuzzy system community to resolve the issue. Existing EFSs are not robust against data uncertainty, temporal system dynamics, and the absence of system order, because a vast majority of EFSs are designed in the type-1 feedforward network architecture. This paper aims to solve the issue of data uncertainty, temporal behavior, and the absence of system order by developing a novel evolving recurrent fuzzy neural network, called evolving type-2 recurrent fuzzy neural network (eT2RFNN). eT2RFNN is constructed in a new recurrent network architecture, featuring double recurrent layers. The new recurrent network architecture evolves a generalized interval type-2 fuzzy rule, where the rule premise is built upon the interval type-2 multivariate Gaussian function, whereas the rule consequent is crafted by the nonlinear wavelet function. The eT2RFNN adopts a holistic concept of evolving systems, where the fuzzy rule can be automatically generated, pruned, merged, and recalled in the single-pass learning mode. eT2RFNN is capable of coping with the problem of high dimensionality because it is equipped with online feature selection technology. The efficacy of eT2RFNN was experimentally validated using artificial and real-world data streams and compared with prominent learning algorithms. eT2RFNN produced more reliable predictive accuracy, while retaining lower complexity than its counterparts.
123 citations
TL;DR: Experimental results demonstrate that the proposed fuzzy regression transfer learning method significantly improves the performance of existing models when tackling regression problems in the target domain.
Abstract: Data science is a research field concerned with processes and systems that extract knowledge from massive amounts of data. In some situations, however, data shortage renders existing data-driven methods difficult or even impossible to apply. Transfer learning has recently emerged as a way of exploiting previously acquired knowledge to solve new yet similar problems much more quickly and effectively. In contrast to classical data-driven machine learning methods, transfer learning methods exploit the knowledge accumulated from data in auxiliary domains to facilitate predictive modeling in the current domain. A significant number of transfer learning methods that address classification tasks have been proposed, but studies on transfer learning in the case of regression problems are still scarce. This study focuses on using transfer learning techniques to handle regression problems in a domain that has insufficient training data. We propose an original fuzzy regression transfer learning method, based on fuzzy rules, to address the problem of estimating the value of the target for regression. A Takagi–Sugeno fuzzy regression model is developed to transfer knowledge from a source domain to a target domain. Experimental results using synthetic data and real-world datasets demonstrate that the proposed fuzzy regression transfer learning method significantly improves the performance of existing models when tackling regression problems in the target domain.
TL;DR: A novel fuzzy inference system on picture fuzzy set called picture inference system (PIS), adapted for all architectures such as the Mamdani, the Sugeno and the Tsukamoto fuzzy inferences is proposed.
Abstract: In this paper, we propose a novel fuzzy inference system on picture fuzzy set called picture inference system (PIS) to enhance inference performance of the traditional fuzzy inference system In PIS, the positive, neutral and negative degrees of the picture fuzzy set are computed using the membership graph that is the combination of three Gaussian functions with a common center and different widths expressing a visual view of degrees Then, the positive and negative defuzzification values, synthesized from three degrees of the picture fuzzy set, are used to generate crisp outputs Learning in PIS including training centers, widths, scales and defuzzification parameters is also discussed The system is adapted for all architectures such as the Mamdani, the Sugeno and the Tsukamoto fuzzy inferences Experimental results on benchmark UCI Machine Learning Repository datasets and an example in control theory - the Lorenz system are examined to verify the advantages of PIS
TL;DR: An extension of the Fuzzy Possibilistic C-Means (FPCM) algorithm using Type-2 FBuzzy Logic Techniques is presented, and this is done in order to improve the efficiency of FPCM algorithm.
Abstract: In this work an extension of the Fuzzy Possibilistic C-Means (FPCM) algorithm using Type-2 Fuzzy Logic Techniques is presented, and this is done in order to improve the efficiency of FPCM algorithm. With the purpose of observing the performance of the proposal against the Interval Type-2 Fuzzy C-Means algorithm, several experiments were made using both algorithms with well-known datasets, such as Wine, WDBC, Iris Flower, Ionosphere, Abalone, and Cover type. In addition some experiments were performed using another set of test images to observe the behavior of both of the above-mentioned algorithms in image preprocessing. Some comparisons are performed between the proposed algorithm and the Interval Type-2 Fuzzy C-Means (IT2FCM) algorithm to observe if the proposed approach has better performance than this algorithm.
TL;DR: In this paper, command filtering-based fuzzy control is designed for uncertain multi-input multioutput (MIMO) nonlinear systems with saturation nonlinearity input and error compensation mechanism is introduced to overcome the drawback of the dynamics surface approach.
Abstract: In this paper, command filtering-based fuzzy control is designed for uncertain multi-input multioutput (MIMO) nonlinear systems with saturation nonlinearity input. First, the command filtering method is employed to deal with the explosion of complexity caused by the derivative of virtual controllers. Then, fuzzy logic systems are utilized to approximate the nonlinear functions of MIMO systems. Furthermore, error compensation mechanism is introduced to overcome the drawback of the dynamics surface approach. The developed method will guarantee all signals of the systems are bounded. The effectiveness and advantages of the theoretic result are obtained by a simulation example.
TL;DR: Based on the powerful stacked generalization principle, a deep TSK fuzzy classifier (D-TSK-FC) is proposed to achieve the enhanced classification accuracy and triplely concise interpretability for fuzzy rules.
Abstract: Although Takagi–Sugeno–Kang (TSK) fuzzy classifier has been applied to a wide range of practical scenarios, how to enhance its classification accuracy and interpretability simultaneously is still a challenging task. In this paper, based on the powerful stacked generalization principle, a deep TSK fuzzy classifier (D-TSK-FC) is proposed to achieve the enhanced classification accuracy and triplely concise interpretability for fuzzy rules. D-TSK-FC consists of base-building units. Just like the existing popular deep learning, D-TSK-FC can be built in a layer-by-layer way. In terms of the stacked generalization principle, the training set plus random shifts obtained from random projections of prediction results of current base-building unit are presented as the input of the next base-building unit. The hidden layer in each base-building unit of D-TSK-FC is represented by triplely concise interpretable fuzzy rules in the sense of randomly selected features with the fixed five fuzzy partitions, random rule combinations, and the same input space kept in every base-building unit of D-TSK-FC. The output layer of each base-building unit can be learnt quickly by least learning machine (LLM). Besides, benefiting from LLM, D-TSK-FC's deep learning can be well scaled up for large datasets. Our extensive experimental results witness the power of the proposed deep TSK fuzzy classifier.
TL;DR: A hybrid OFBAT-RBFL heart disease diagnosis system is designed and the experimentation result proves that the RBFL prediction algorithm outperformed the existing approach by attaining the accuracy of 78%.
Abstract: The objective of the work is to predict heart disease using computing techniques like an oppositional firefly with BAT and rule-based fuzzy logic (RBFL) The system would help the doctors to automate heart disease diagnosis and to enhance the medical care In this paper, a hybrid OFBAT-RBFL heart disease diagnosis system is designed Here, at first, the relevant features are selected from the dataset using locality preserving projection (LPP) algorithm which helps the diagnosis system to develop a classification model using the fuzzy logic system After that, the rules for the fuzzy system are created from the sample data Among the entire rules, the important and relevant group of rules are selected using OFBAT algorithm Here, the opposition based learning (OBL) is hybrid to the firefly with BAT algorithm to improve the performance of the FAT algorithm while optimizing the rules of the fuzzy logic system Next, the fuzzy system is designed with the help of designed fuzzy rules and membership functions so that classification can be carried out within the fuzzy system designed At last, the experimentation is performed by means of publicly available UCI datasets, ie, Cleveland, Hungarian and Switzerland datasets The experimentation result proves that the RBFL prediction algorithm outperformed the existing approach by attaining the accuracy of 78%
TL;DR: A new hybrid method based on enhanced fuzzy multi-criteria collaborative filtering which incorporates demographic information and an item-based ontological semantic filtering approach for movie recommendation purposes is proposed.
TL;DR: The experimental results revealed that the classification with level-8 partitioning provides a classification accuracy of 97.67 %, with a sensitivity and specificity of 96.07 and 100 %, respectively.
Abstract: Crohn's disease (CD) diagnosis is a tremendously serious health problem due to its ultimately effect on the gastrointestinal tract that leads to the need of complex medical assistance. In this study, the backpropagation neural network fuzzy classifier and a neuro-fuzzy model are combined for diagnosing the CD. Factor analysis is used for data dimension reduction. The effect on the system performance has been investigated when using fuzzy partitioning and dimension reduction. Additionally, further comparison is done between the different levels of the fuzzy partition to reach the optimal performance accuracy level. The performance evaluation of the proposed system is estimated using the classification accuracy and other metrics. The experimental results revealed that the classification with level-8 partitioning provides a classification accuracy of 97.67 %, with a sensitivity and specificity of 96.07 and 100 %, respectively.
TL;DR: A neuro-fuzzy system that implements differential neural networks (DNNs) as consequences of Takagi–Sugeno (T–S) fuzzy inference rules is proposed, and the DNNs substitute the local linear systems that are used in the common T–S method.
Abstract: The identification problem incorporated in feedback control of uncertain nonlinear systems exhibiting complex behavior has been solved in different ways. Some of these solutions have used artificial intelligence methods like fuzzy logic and neural networks. However, their individual implementation suffers from certain drawbacks, such as the black-box nature of neural network and the problem of finding suitable membership functions for fuzzy systems. These weaknesses can be avoided by implementing a hybrid structure combining these two approaches, the so-called neuro-fuzzy system. In this paper, a neuro-fuzzy system that implements differential neural networks (DNNs) as consequences of Takagi–Sugeno (T–S) fuzzy inference rules is proposed. The DNNs substitute the local linear systems that are used in the common T–S method. In this paper, DNNs are used to provide an effective instrument for dealing with the identification of the uncertain nonlinear system, while the T–S rules are used to provide the framework of previous knowledge of the system. The main idea is to carry out an online identification process of an uncertain nonlinear system with the aim to design a closed-loop trajectory tracking controller. The methodology developed in this study that supports the identification and trajectory control designs is based on the Lyapunov formalism. The DNN implementation results in a time-varying T–S system. As a consequence, the solution of two time-varying Riccati equations was used to adjust the learning laws in the DNN as well as to adjust the gains of the controller. Two results were provided to justify the existence of positive-definite solutions for the class of Riccati equations used in the learning laws of DNNs. A complete description of the learning laws used for the set of DNN identifiers is also obtained. An autonomous underwater vehicle system is used to demonstrate the performance of the controller on tracking a desired 3-D path by this combination of the DNN and the T–S system.
TL;DR: F fuzzy set theory with respect to subjective expert opinion is employed to cope with the uncertain knowledge of BEs including randomness, ignorance, and shortages of data in failure probability analysis.
Abstract: There are many available techniques which are widely used for failure probability analysis. Fault tree analysis (FTA) is a well-known method to identify the basic events (BEs) to reach top event. However, the FTA method in real circumstances is limited because of the many unknown and the vagueness of the situations. Thus, fuzzy set theory with respect to subjective expert opinion is employed to cope with the uncertain knowledge of BEs including randomness, ignorance, and shortages of data. In addition, to gain this purpose, much subjectivity may appear; as an example, the main one is the expert weighting. This study highlights the utility of fuzzy set theory and analytic hierarchy process to failure probability analysis in a case study. A chemical process plant has been selected to illustrate the application of proposed model with a comparison of the results with conventional model.
TL;DR: This study significantly improves the efficacy of the existing adaptive system by exploiting more information during both the diagnosis and modification processes by taking into account observations, rules, and interpolation procedures, all as diagnosable and modifiable system components.
Abstract: As a substantial extension to fuzzy rule interpolation that works based on two neighboring rules flanking an observation, adaptive fuzzy rule interpolation is able to restore system consistency when contradictory results are reached during interpolation. The approach first identifies the exhaustive sets of candidates, with each candidate consisting of a set of interpolation procedures which may jointly be responsible for the system inconsistency. Then, individual candidates are modified such that all contradictions are removed, and thus, interpolation consistency is restored. It has been developed on the assumption that contradictions may only be resulted from the underlying interpolation mechanism, and that all the identified candidates are not distinguishable in terms of their likelihood to be the real culprit. However, this assumption may not hold for real-world situations. This paper, therefore, further develops the adaptive method by taking into account observations, rules, and interpolation procedures, all as diagnosable and modifiable system components. In addition, given the common practice in fuzzy systems that observations and rules are often associated with certainty degrees, the identified candidates are ranked by examining the certainty degrees of its components and their derivatives. From this, the candidate modification is carried out based on such ranking. This study significantly improves the efficacy of the existing adaptive system by exploiting more information during both the diagnosis and modification processes.
TL;DR: In this paper, a methodology that combines principal component analysis (PCA) with adaptive neuro fuzzy based inference system (ANFIS) is proposed to model the nonlinear relationship between ground surface settlements induced by an earth pressure balanced TBM and the operational and geological parameters.
TL;DR: By a newly proposed inequality bounding technique, the fuzzy sampled-data filtering performance analysis is carried out such that the resultant neural networks is asymptotically stable.
TL;DR: The basic concepts of data mining are introduced, including data mining technology, artificial intelligence, machine learning, statistical analysis, fuzzy logic, pattern recognition and artificial neural networks and other technologies, and the development direction of the intelligent system is described.
TL;DR: A novel result is established to ensure the finite-time stability of the addressed system by employing the differential inequality techniques and numerical simulations to demonstrate the theoretical result.
TL;DR: This work introduces a fuzzy particle swarm reinforcement learning approach that can construct fuzzy RL policies solely by training parameters on world models that simulate real system dynamics, and is the first to relate self-organizing fuzzy controllers to model-based batch RL.
TL;DR: A hybrid model comprising both methods’ advantages is presented, and the result comparison indicates that the ANFIS is more accurate than artificial neural networks (ANN) and SARimA-ANN models, and SARIMA-ANfIS is the superior model among all.
Abstract: Regarding the complexity and limitations of current knowledge, monthly inflow prediction is often not sufficiently accurate and cannot fulfil the needs in water resource planning. Such time series consist of periodic and random components. Thus, by using data pre-processing methods, it is possible to reduce the problematic effects of these components in the modeling process. Monthly inflow methods encompass statistical and soft computing methods. Each of these methods has advantages and disadvantages. In this study, a hybrid model comprising both methods’ advantages is presented. This four-step model includes seasonal autoregressive integrated moving average (SARIMA) and adaptive neuro fuzzy inference systems (ANFIS), which is a new hybrid model (SARIMA-ANFIS). The first step entails data pre-processing to prepare the data for linear component modeling. In the second step, the linear and nonlinear terms are estimated by the SARIMA model. In the third step, some goodness of fit tests are applied to investigate the validity of the linear and nonlinear components of decomposed inflows and SARIMA model parameters. Upon the confident correct selection of components, in the fourth step the nonlinear components are modeled by ANFIS. In this method, ANN modeling is used instead of ANFIS (SARIMA-ANN model). The result comparison indicates that the ANFIS is more accurate than artificial neural networks (ANN) and SARIMA-ANN models, and SARIMA-ANFIS is the superior model among all.
TL;DR: A Energy Aware Clustering using Neuro-fuzzy approach (EACNF) is proposed to form finest and energy aware clusters in WSN that handling Trust factor for security to the network and experiment results show that EACNF performs better than the other related schemes.
Abstract: Nowadays sensor plays an important role in the day today life. People uses wireless technology along with sensor for monitoring home held devices, security alerts, natural disasters alert, building supervision, industrial quality management, etc. Wireless Sensor Network (WSN) consists of thousands of economical and feasible disposable sensors, deployed in the environment to sense parameters related to the surroundings such as temperature, moisture level, pressure etc., Number of sensor nodes are connected in these networks for communication. Each nodes are self-organized, having the capacity of sense, process, and aggregate data. Energy utilization in WSN is major issue in networks for improving network lifetime. Conventional clustering schemes are created with static cluster heads that die past than the normal nodes that degrade the network performance in routing. It is very vital area to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network life time. In this paper, a Energy Aware Clustering using Neuro-fuzzy approach (EACNF) is proposed to form finest and energy aware clusters. The proposed scheme consists of fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. EACNF used neural network that provide effective training set related to energy and density of all nodes to estimate the expected energy for Uncertain cluster heads. Sensor nodes with higher energy are trained with various location of base station to select energy aware cluster heads. Fuzzy if–then mapping rule is used in fuzzy logic part that inputs to form clusters and cluster heads. EACNF is designed for WSN that handling Trust factor for security to the network. EACNF used three metric such as transmission range, residual energy and Trust factor for improving network life time. The proposed scheme EACNF is compared with related clustering schemes namely Cluster-Head Election Mechanism using Fuzzy Logic and Energy-Aware Fuzzy Unequal Clustering. The experiment results show that EACNF performs better than the other related schemes.
TL;DR: It is shown that type-2 fuzzy sets outperform both traditional image processing techniques as well as techniques using type-1 fuzzy sets, and provide the ability to handle uncertainty when the image is corrupted by noise.
Abstract: This paper presents a literature review of applications using type-2 fuzzy systems in the area of image processing. Over the last years, there has been a significant increase in research on higher-order forms of fuzzy logic; in particular, the use of interval type-2 fuzzy sets and general type-2 fuzzy sets. The idea of making use of higher orders, or types, of fuzzy logic is to capture and represent uncertainty that is more complex. This paper is focused on image processing systems, which includes image segmentation, image filtering, image classification and edge detection. Various applications are presented where general type-2 fuzzy sets, interval type-2 fuzzy sets, and interval-value fuzzy sets are used; some are compared with the traditional type-1 fuzzy sets and others methodologies that exist in the literature for these areas in image processing. In all accounts, it is shown that type-2 fuzzy sets outperform both traditional image processing techniques as well as techniques using type-1 fuzzy sets, and provide the ability to handle uncertainty when the image is corrupted by noise.