Interval Type-2 Neuro-fuzzy Inference System Research Articles

A Self-Regulated Interval Type-2 Neuro-Fuzzy Inference System for Handling Nonstationarities in EEG Signals for BCI

This paper addresses the key problems of nonstationarity and influence of artifacts in electroencephalogram (EEG)-based brain-computer-interface (BCI) systems. The nonstationary nature of EEG data arises due to the physiological/instrumental differences in intra/intersession of the data generation process. This paper proposes a robust common spatial pattern feature extraction algorithm (RoCSP) to overcome the effects of artifacts and a self-regulated interval type-2 neuro-fuzzy inference system (SRIT2NFIS) to handle this inherent nonstationarity. Combined together, this approach is referred to as RoCSP-SRIT2NFIS. The RoCSP algorithm provides better features than the CSP algorithm by excluding those trials that are affected by the artifacts. SRIT2NFIS uses the features generated by the RoCSP algorithm as input and handles the nonstationarity as an uncertainty using the interval type-2 fuzzy sets in the antecedent of fuzzy rules. A self-regulatory learning mechanism is used to evolve the structure automatically and learn the parameters of the network. A regularized projection-based learning algorithm and a modified rule addition criterion are also proposed to improve the generalization performance of SRIT2NFIS. Using benchmark datasets, performance evaluation has been carried out, and the results indicate that, compared with other existing algorithms, RoCSP-SRIT2NFIS produces higher classification accuracy of 3-5% in simple tasks like left-right classification and 6-8% in complex tasks like foot-tongue classification. In addition, a statistical analysis of the performance results indicates that RoCSP-SRIT2NFIS performs better and is more suitable for an efficient BCI.

An interval type-2 fuzzy inference system and its meta-cognitive learning algorithm

In this paper, we propose an evolving interval type-2 neuro-fuzzy inference system and its meta-cognitive learning algorithm. The antecedent of each rule is an interval type-2 fuzzy set and the consequent realizes the Takagi–Sugeno–Kang inference mechanism. It employs a data driven mechanism for type-reduction and uncertainty is modeled using both the antecedent and consequent parameters. The learning starts with zero rules and evolves the network parameters and structure as a new sample arrives, using a meta-cognitive learning algorithm. The meta-cognitive learning algorithm regulates learning by employing adequate learning strategies. It uses gradient-descent to adapt the network parameters. The proposed algorithm is referred to as meta-cognitive interval type-2 neuro-fuzzy inference system-gradient descent (McIT2FIS-GD). The performance of McIT2FIS-GD is evaluated using a set of benchmark and a real-world wind speed prediction problem and compared with existing type-1 and type-2 systems. The results highlight that performance of McIT2FIS-GD is superior than other compared algorithms.

An Evolving Interval Type-2 Neurofuzzy Inference System and Its Metacognitive Sequential Learning Algorithm

In this paper, we propose an evolving interval type-2 neurofuzzy inference system (IT2FIS) and its fully sequential learning algorithm. IT2FIS employs interval type-2 fuzzy sets in the antecedent part of each rule and the consequent realizes Takagi–Sugeno–Kang fuzzy inference mechanism. In order to render the inference fast and accurate, we propose a data-driven interval-reduction approach to convert interval type-1 fuzzy set in antecedent to type-1 fuzzy number in the consequent. During learning, the sequential algorithm learns a sample one-by-one and only once. The IT2FIS structure evolves automatically and adapts its network parameters using metacognitive learning mechanism concurrently. The metacognitive learning regulates the learning process by appropriate selection of learning strategies and helps the proposed IT2FIS to approximate the input–output relationship efficiently. An evolving IT2FIS employing a metacognitive learning algorithm is referred to as McTI2FIS. Performance of metacognitive interval type-2 neurofuzzy inference system (McIT2FIS) is evaluated using a set of benchmark time-series problems and is compared with existing type-2 and type-1 fuzzy inference systems. Finally, the performance of the proposed McIT2FIS has been evaluated using a practical stock price-tracking problem. The results clearly highlight that McIT2FIS performs better than other existing results in the literature.

A Metacognitive Complex-Valued Interval Type-2 Fuzzy Inference System

This paper presents a complex-valued interval type-2 neuro-fuzzy inference system (CIT2FIS) and derive its metacognitive projection-based learning (PBL) algorithm. Metacognitive CIT2FIS (Mc-CIT2FIS) consists of a CIT2FIS, which realizes Takagi-Sugeno-Kang type inference mechanism, as its cognitive component. A PBL with self-regulation is its metacognitive component. The rules of CIT2FIS employ interval type-\(2~q\) -Gaussian membership functions that can represent different radial basis functions for different values of \(q\) . As each sample is presented to the network, the metacognitive component monitors the hinge-loss error and class-specific knowledge potential of the current sample to efficiently decide on what-to-learn, when-to-learn, and how-to-learn it. When a new rule is added or existing rules are updated, the optimal parameters of CIT2FIS corresponding to the minimum of the hinge-loss error function are computed using a PBL algorithm derived using the Wirtinger calculus. The performance of Mc-CIT2FIS is evaluated on a set of benchmark real-valued classification problems from the UCI machine learning repository. A circular transformation is used to convert the real-valued features to the complex-valued features in these problems. The performance comparison and statistical study clearly show the superior classification ability of Mc-CIT2FIS. Finally, the proposed complex-valued network is used to solve a practical human action recognition problem that is represented by complex-valued optical flow-based feature set, and a human emotion recognition problem represented using complex-valued Gabor filter-based features. The performance results on these problems substantiate the superior classification ability of Mc-CIT2FIS.

A meta-cognitive interval type-2 fuzzy inference system and its projection based learning algorithm

A meta-cognitive interval type-2 neuro-fuzzy inference system (McIT2FIS) based classifier and its projection based learning algorithm is presented in this paper. McIT2FIS consists of two components, namely, a cognitive component and a meta-cognitive component. The cognitive component is an interval type-2 neuro-fuzzy inference system (IT2FIS) represented as a six layered adaptive network realizing Takagi-Sugeno-Kang type inference mechanism. A self-regulatory learning mechanism forms the meta-cognitive component. IT2FIS begins with zero rules, and rules are added and updated depending on the prediction error and relative knowledge contained the current sample. As each sample is presented to the network, the meta-cognitive component monitors the hinge-loss error and class-specific spherical potential of the current sample to decide what-to-learn, when-to-learn and how-to-learn them, efficiently. When a new rule is added or when an existing rule is updated, a projection based learning algorithm computes the optimal output weights with least computational effort by finding analytical minima of the nonlinear energy function. It uses class specific criterion and sample overlap criterion to estimate the network parameters corresponding to the minimum energy point of the error function. Moreover, consistently under - performing rules are pruned from the network leading to a compact network. The performance of McIT2FIS is first evaluated on a set of benchmark classification problems from UCI machine learning repository. A tenfold cross validation based performance comparison with other state-of-the-art approaches indicates its improved performance. Next, its performance is evaluated on detection of attention deficiency hyperactivity disorder (ADHD) in children. The aim of this study is to classify a child as having typically developing controls or as an ADHD patient. Voxel based features extracted from amygdala region of the brain is employed in this study. The network is trained and tested on samples obtained from ADHD-200 consortium dataset consisting of 941 subjects. The performance comparison with standard support vector machine shows that McIT2FIS has superior classification ability than SVM in diagnosing ADHD.

Hierarchical type-2 neuro-fuzzy BSP model

This paper presents a novel hybrid interval type-2 neuro-fuzzy inference system, with automatic learning of all its parameters, to handle uncertainty. This new model, called hierarchical type-2 neuro-fuzzy BSP model (T2-HNFB), combines the paradigms of the type-2 fuzzy inference systems and neural networks with recursive partitioning techniques (binary space partitioning – BSP). The model is able to automatically create and expand its own structure, to reduce limitations on the number of inputs and to extract fuzzy linguistic rules from a dataset, as well as to efficiently model and manipulate most types of uncertainty existing in real situations. In addition, it provides an interval for its output, which can be regarded as a measure of uncertainty and constitutes important information for real applications. In this context, this model overcomes the limitations of the conventional type-2 and type-1 fuzzy inference systems. Experimental results show that the results provided by the T2-HNFB model are close to and in several cases better than the best results supplied by the other models used for comparison.