Sau Wai Tung

eT2FIS: An Evolving Type-2 Neural Fuzzy Inference System

There are two main approaches to design a neural fuzzy system; namely, through expert knowledge, and through numerical data. While the computational structure of a system is manually crafted by human experts in the former case, self-organizing neural fuzzy systems that are able to automatically extract generalized knowledge from batches of numerical training data are proposed for the latter. Nevertheless, both of these approaches are static where only parameters of a system are updated during training. On the other hand, the demands and complexities of real-life applications often require a neural fuzzy system to adapt both its parameters and structure to model the changing dynamics of the environment. To counter these modeling bottlenecks, intense research efforts are subsequently channeled into the studies of evolving/online neural fuzzy systems. There are generally two classes of evolving neural fuzzy systems: the Takagi-Sugeno-Kang (TSK) systems and the Mamdani systems. While most existing literature consists of evolving Type-1 TSK-typed and Type-1 Mamdani-typed models, they may not perform well in noisy environment. To improve the robustness of these neural fuzzy systems, recent efforts have been directed to extend evolving Type-1 TSK-typed neural fuzzy systems to Type-2 models because of their better known noise resistance abilities. In contrast, minimum similar effort has been made for evolving Mamdani-typed models. In this paper, we present a novel evolving Type-2 Mamdani-typed neural fuzzy system to bridge this gap. The proposed system is named evolving Type-2 neural fuzzy inference system (eT2FIS), and it employs a data-driven incremental learning scheme. Issues involving the online sequential learning of the eT2FIS model are carefully examined. A new rule is created when a newly arrived data is novel to the present knowledge encrypted; and an obsolete rule is deleted when it is no longer relevant to the current environment. Highly over-lapping fuzzy labels in the input-output spaces are merged to reduce the computational complexity and improve the overall interpretability of the system. By combining these three operations, eT2FIS is ensured a compact and up-to-date fuzzy rule base that is able to model the current underlying dynamics of the environment. Subsequently, the proposed eT2FIS model is employed in a series of benchmark and real-world applications to demonstrate its efficiency as an evolving neural fuzzy system, and encouraging performances have been achieved.

SaFIN: A Self-Adaptive Fuzzy Inference Network

There are generally two approaches to the design of a neural fuzzy system: (1) design by human experts, and (2) design through a self-organization of the numerical training data. While the former approach is highly subjective, the latter is commonly plagued by one or more of the following major problems: (1) an inconsistent rulebase; (2) the need for prior knowledge such as the number of clusters to be computed; (3) heuristically designed knowledge acquisition methodologies; and (4) the stability-plasticity tradeoff of the system. This paper presents a novel self-organizing neural fuzzy system, named Self-Adaptive Fuzzy Inference Network (SaFIN), to address the aforementioned deficiencies. The proposed SaFIN model employs a new clustering technique referred to as categorical learning-induced partitioning (CLIP), which draws inspiration from the behavioral category learning process demonstrated by humans. By employing the one-pass CLIP, SaFIN is able to incorporate new clusters in each input-output dimension when the existing clusters are not able to give a satisfactory representation of the incoming training data. This not only avoids the need for prior knowledge regarding the number of clusters needed for each input-output dimension, but also allows SaFIN the flexibility to incorporate new knowledge with old knowledge in the system. In addition, the self-automated rule formation mechanism proposed within SaFIN ensures that it obtains a consistent resultant rulebase. Subsequently, the proposed SaFIN model is employed in a series of benchmark simulations to demonstrate its efficiency as a self-organizing neural fuzzy system, and excellent performances have been achieved.

Motor imagery BCI for upper limb stroke rehabilitation: An evaluation of the EEG recordings using coherence analysis

Brain-computer interface (BCI) technology has the potential as a post-stroke rehabilitation tool, and the efficacy of the technology is most often demonstrated through output peripherals such as robots, orthosis and computers. In this study, the EEG signals recorded during the course of upper limb stroke rehabilitaion using motor imagery BCI were analyzed to better understand the effect of BCI therapy for post-stroke rehabilitation. The stroke patients recruited underwent 10 sessions of 1-hour BCI with robotic feedback for 2 weeks, 5 times a week. The analysis was performed by computing the coherences of the EEG in the lesion and contralesion side of the hemisphere from each session, and the coherence index of the lesion hemisphere (0 ≤ CI ≤ 1) was computed. The coherence index represents the rate of activation of the lesion hemisphere, and the correlation with the Fugl-Meyer assessment (FMA) before and after the BCI therapy was investigated. Significant improvement in the FMA scores was reported for five of the six patients (p = 0.01). The analysis showed that the number of sessions with CI ≥ 0.5 correlated with the change in the FMA scores. This suggests that post-stroke motor recovery best results from the activation in the lesion hemisphere, which is in agreement with previous studies performed using multimodal imaging technologies.

SoHyFIS-Yager: A self-organizing Yager based Hybrid neural Fuzzy Inference System

The Hybrid neural Fuzzy Inference System (HyFIS) is a multilayer adaptive neural fuzzy system for building and optimizing fuzzy models using neural networks. In this paper, the fuzzy Yager inference scheme, which is able to emulate the human deductive reasoning logic, is integrated into the HyFIS model to provide it with a firm and intuitive logical reasoning and decision-making framework. In addition, a self-organizing gaussian Discrete Incremental Clustering (gDIC) technique is implemented in the network to automatically form fuzzy sets in the fuzzification phase. This clustering technique is no longer limited by the need to have prior knowledge about the number of clusters present in each input and output dimensions. The proposed self-organizing Yager based Hybrid neural Fuzzy Inference System (SoHyFIS-Yager) introduces the learning power of neural networks to fuzzy logic systems, while providing linguistic explanations of the fuzzy logic systems to the connectionist networks. Extensive simulations were conducted using the proposed model and its performance demonstrates its superiority as an effective neuro-fuzzy modeling technique.

HyFIS-Yager-gDIC: a self-organizing hybrid neural fuzzy inference system realizing Yager inference

The Hybrid neural Fuzzy Inference System (HyFIS) is a five layers adaptive neural fuzzy system for building and optimizing fuzzy models. In this paper, the fuzzy Yager inference scheme, which accounts for a firm and intuitive logical framework that emulates the human reasoning and decision-making mechanism, is integrated into the HyFIS network. In addition, a self-organizing gaussian Discrete Incremental Clustering (gDIC) technique is used to form the fuzzy sets in the fuzzification phase. This clustering technique is no longer limited by the need to have prior knowledge about the number of clusters needed in each input and output dimensions. The proposed self-organizing Hybrid neural Fuzzy Inference System based on Yager inference (HyFIS-Yager-gDIC) is benchmarked on two case studies to demonstrate its superiority as an effective neuro-fuzzy modelling technique.

T2-HyFIS-yager: Type 2 hybrid neural fuzzy inference system realizing yager inference

The Hybrid neural Fuzzy Inference System (Hy-FIS) is a five layers adaptive neural fuzzy inference system, based on the Compositional Rule of Inference (CRI) scheme, for building and optimizing fuzzy models. To provide the HyFIS architecture with a firmer and more intuitive logical framework that emulates the human reasoning and decision-making mechanism, the fuzzy Yager inference scheme, together with the self-organizing gaussian Discrete Incremental Clustering (gDIC) technique, were integrated into the HyFIS network to produce the HyFIS-Yager-gDIC . This paper presents T2-HyFIS-Yager, a Type-2 Hybrid neural Fuzzy Inference System realizing Yager inference, for learning and reasoning with noise corrupted data. The proposed T2-HyFIS-Yager is used to perform time-series forecasting where a non-stationary time-series is corrupted by additive white noise of known and unknown SNR to demonstrate its superiority as an effective neuro-fuzzy modeling technique.

FAPOP: Feature analysis enhanced pseudo outer-product fuzzy rule identification system

Most existing neural fuzzy systems either overlook the importance of feature analysis; or it is performed as a separate phase prior to the design stage of the systems. This paper proposes a novel neural fuzzy system, named Feature Analysis Enhanced Pseudo Outer-Product Fuzzy Rule Identification System (FAPOP), which integrates its design with feature analysis. The objective is two-folds; namely, (1) to improve the interpretability of the system by identifying features relevant to its computational structure; and (2) to improve the accuracy of the system by identifying features relevant to the application problem. The proposed FAPOP model is subsequently employed in a series of benchmark simulations to demonstrate its efficiency as a neural fuzzy modeling system, and excellent performances have been achieved.

A measurement of motor recovery for motor imagery-based BCI using EEG coherence analysis

Motor imagery-based BCI (MI-BCI) technology possesses the potential to be a post-stroke rehabilitation tool. To ensure the optimal use of the MI-BCI technology for stroke rehabilitation, the ability to measure the motor recovery patterns is important. In this study, the relationship between the EEG recorded during, and the changes in the recovery patterns before and after MI-BCI rehabilitation is investigated. Nine stroke patients underwent 10 sessions of 1 hour MI-BCI rehabilitation with robotic feedback for 2 weeks, 5 times a week. The coherence index (0 ≤ CI ≤ 1), which is an EEG metric comparing the coherences of the EEG in the ipsilesioned hemisphere with that in the contralesioned hemisphere, was computed for each session for the first week. Pre- and post-rehabilitation motor functions were measured with the Fugl-Meyer assessment (FMA). The number of sessions with CI greater than a unique subject-dependent baseline value ζ correlated with the change in the FMA scores (R = 0.712, p = 0.031). Subsequently, a leave-one-out approach resulted in a prediction mean squared error (MSE) of 15.1 using the established relationship. This result is better compared to using the initial FMA score as a predictor, which gave a MSE value of 18.6. This suggests that CI computed from EEG may have a prognostic value for measuring the motor recovery for MI-BCI.

Traffic modeling and identification using a Self-adaptive Fuzzy Inference Network

Traffic modeling and identification is an important aspect of traffic control today. With an increase in the demands on todays transportation network, an efficient system to model and understand the changes in the network is necessary for policy makers to make timely decisions which affect the overall level of service experienced by commuters. This paper proposes a novel approach to traffic modeling and identification using a Self-adaptive Fuzzy Inference Network (SaFIN). The study is performed on a set of real world traffic data collected along the Pan Island Expressway (PIE) in Singapore. By applying a hybrid fuzzy neural network in the traffic modeling task, SaFIN is able to capitalize on the functionalities of both the fuzzy system and the neural network to (1) provide meaningful and intuitive insights to the traffic data, and (2) demonstrate excellent modeling and identification capabilities for highly nonlinear traffic flow conditions.