Antecedent Part Of Fuzzy Rules Research Articles

An extension of rule base evidential reasoning in the interval-valued intuitionistic fuzzy setting applied to the type 2 diabetes diagnostic

This paper presents an interval-valued intuitionistic fuzzy extension of rule base evidential reasoning which generally is a synthesis of fuzzy logic, the Dempster–Shafer theory of evidence (DST) and Atanassov’s intuitionistic fuzzy sets (A-IFS) theory redefined in the framework of DST. A lot of attention is paid to situations when in the solution of decision making problem the competing fuzzy classes with intersecting membership functions in antecedent parts of fuzzy rules, e.g., such as Low and Moderate play an important role in the problem formulation. As a result, a new mathematical object “belief interval bounded belief interval” (BIBBI) is obtained. The properties of BIBBI make it possible to use it as a more reliable representation of interval-valued intuitionistic fuzzy value than that formulated in terms of A-IFS. Using the introduced DST based definition of interval-valued A-IFS, the corresponding operations with BIBBIs and the DST representation of rule base evidential reasoning in the intuitionistic fuzzy setting, a new approach to the interval-valued intuitionistic fuzzy extension of rule base evidential reasoning is developed. To prove the correctness and applicability of this approach to the solution of decision making problems, a case study of its use for the type 2 diabetes diagnostics is analyzed.

An Interpretable Fuzzy DBN-Based Classifier for Indoor User Movement Prediction in Ambient Assisted Living Applications

In this paper, an interpretable fuzzy deep belief network (DBN)-based classifier called deep belief networks-based Takagi-Sugeno-Kang fuzzy classifier (DBN-TSK-FC) is created for indoor user movement prediction in ambient assisted living applications. With its promising classification performance, DBN-TSK-FC features sharing both the powerful neural representation ability of a DBN and the strong uncertainty-handling capability of an interpretable fuzzy representation. On the one hand, DBN-TSK-FC builds its interpretable fuzzy representation in a hierarchical way by applying the classical fuzzy clustering algorithm FCM to obtain fuzzy partitions on the training dataset. Then, it forms interpretable antecedent parts of fuzzy rules as the corresponding fuzzy representation. On the other hand, DBN-TSK-FC builds its DBN-based neural representation in the other hierarchical way. That is, it applies the existing unsupervised DBN pretraining on the training dataset and then takes the neural representation of all the hidden nodes in the top layer of the corresponding DBN as the set of consequent variables of fuzzy rules. In this approach, both the interpretable fuzzy representation and the DBN-based neural representation are further fused to form the corresponding fuzzy rules quickly by using the least learning machine (LLM) on both the fuzzy rules and the labeling information of the original dataset. Therefore, DBN-TSK-FC is essentially a deep TSK fuzzy classifier from the perspective of fuzzy rules, and it indeed avoids the very slow fine-tuning training required after the unsupervised pretraining of the existing DBN learning. The experimental results on the MovementAAL_RSS dataset indicate the effectiveness of the proposed classifier DBN-TSK-FC.

Radar Emitter Signals Identification with a Optimal Recurrent Type 2 Wavelet Fuzzy Neural Network

One optimal recurrent type 2-wavelet fuzzy neural network (RT2WFNN) is proposed in this paper to deal with the problem of emitter identification caused by some types of noise. The RT2WFNN has both an on-line parameter and structure learning ability. The new adaptive method based on gradient decent with Lyapunov theorem used for parameter learning can improve the learning ability of RT2WFNN; the type 2 FCM and optimal improved EKM algorithm are used to select the cluster centers of the membership functions in the antecedent part of fuzzy rules of the RT2WFNN for shortening the adjustment time. A number of simulations are presented to demonstrate the identification capability of the RT2WFNN algorithm for the radar emitter identification. The simulation and actual experimental results illustrated that the proposed RT2WFNN cannot only have better identification capability, but also is relatively more insensitive to noise and accelerate the computing speed.

Non-iterative T–S fuzzy modeling with random hidden-layer structure for BFG pipeline pressure prediction

The pressure of blast furnace gas (BFG) pipeline is one of the most significant criteria for the scheduling of byproduct gas system in steel industry. Given the measuring problems under current industrial conditions, a non-iterative T–S fuzzy model is established in this study for on-line modeling. Firstly, a random hidden-layer structure for the antecedent part of fuzzy rules is designed for fuzzy partitioning, which improves the efficiency of on-line training and makes the model adaptive for industrial application. A fast sparse coding identification method is then proposed to determine the consequent parameters of the fuzzy rules, which is capable of enhancing the robustness and the generalization performance under various production situations. To verify the effectiveness of the proposed method, the noise additive Mackey–Glass time series data and the practical data coming from a steel plant are employed. Furthermore, the statistics results from the practice indicate that the proposed method has good adaptability and can provide reliable measurement results for industrial applications.

Differences between t-norms in fuzzy control

The application of conjunctive aggregation functions in fuzzy control systems with n inputs is discussed, and the effect of the choice of a continuous t-norm in the inference phase for Takagi–Sugeno–Kang (TSK) systems is computed. A continuous t-norm modeling AND connective in antecedent part of fuzzy rules can be reduced just to strict or nilpotent t-norm. The isomorphism of strict (nilpotent) t-norms enables simpler fitting of TSK fuzzy system parameters and reduces the computational complexity. Similar principle can also be used in the case of some noncommutative conjunctive aggregation functions modeling AND connective. The effect of the choice of a continuous t-norm is then evaluated on well-known case studies in fuzzy control, the Sinc function, and the urban traffic noise control system.

Weighted Fuzzy Interpolative Reasoning Based on Weighted Increment Transformation and Weighted Ratio Transformation Techniques

In this paper, we present a new weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems. The proposed method uses weighted increment transformation and weighted ratio transformation techniques to handle weighted fuzzy interpolative reasoning in sparse fuzzy rule-based systems. It allows each variable that appears in the antecedent parts of fuzzy rules to associate with a weight between zero and one. Moreover, we also propose an algorithm that automatically tunes the optimal weights of the antecedent variables appearing in the antecedent parts of fuzzy rules. We also apply the proposed weighted fuzzy interpolative reasoning method to handle the truck backer-upper control problem. The proposed weighted fuzzy interpolative reasoning method performs better than the ones obtained by the traditional fuzzy inference system (2000), Huang and Shen's method (2008), and Chen and Ko's method (2008). The proposed method provides us with a useful way to deal with weighted fuzzy interpolative reasoning in sparse fuzzy rule-based systems.

Obstacle Avoidance of a Mobile Robot Using Hybrid Learning Approach

in this paper, a hybrid learning approach for obstacle avoidance of a mobile robot is presented. the key features of the approach are, firstly, innate hardwired behaviors which are used to bootstrap learning in the mobile robot system. a neuro-fuzzy controller is developed from a pre-wired or innate controller based on supervised learning in a simulation environment. the fuzzy inference system has been constructed based on the generalized dynamic fuzzy neural networks learning algorithm of Wu and Er, whereby structure and parameters identification are carried out automatically and simultaneously. Secondly, the neuro-fuzzy controller is capable of re-adapting in a new environment. After carrying out the learning phase on a simulated robot, the controller is implemented on a real robot. A reinforcement learning method based on the fuzzy actor-critic learning algorithm is employed so that the system can re-adapt to a new environment without human intervention. In this phase, the structure of the fuzzy inference system and the parameters of the antecedent parts of fuzzy rules are frozen, and reinforcement learning is applied to further tune the parameters in the consequent parts of the fuzzy rules. Through the hybrid learning approach, an efficient and compact neuro-fuzzy system is generated for obstacle avoidance of a mobile robot in the real world.

Research on the Method of Neural Network Modeling Based on FCM Algorithm and Its Application on Vision-based Sensors

This paper proposes a novel fuzzy neural network model based on fuzzy clustering method. The model can accept continuous and discrete inputs together; the discrete input to the model is divided into several clusters by using fuzzy c-mean clustering algorithm (FCM). A fuzzy clustering neuron (FC-neuron) is designed to calculate a membership degree value belonging to one cluster for each discrete input. A four-layer hybrid neural network is constructed; fuzzy-neurons and FC-neurons construct the antecedent part of fuzzy rules. A multi-input multioutput hybrid neural network was designed by the novel modeling method and applied to vision-based sensors. Simulation results show this method is superior to the traditional neural network model in vision-based sensors.

삼각형 소속함수로 구성된 퍼지시스템의 고속 퍼지추론 알고리즘

퍼지이론의 응용은 대부분 퍼지추론을 이용하는 것이다. 그러나 퍼지추론은 입력변수의 수가 많아지거나 각 입력변수에 많은 수의 퍼지라벨을 설정할 경우 그 추론에 필요한 계산시간이 많아지게 되며 이러한 것은 컴퓨터 연산의 대수곱(arithmetic product)의 수에 의해 결정된다. 더구나 퍼지추론의 응용이 가장 활발한 퍼지제어분야에서는 이러한 추론시간은 실제 시스템에 적용 시 가장 큰 제약조건이 된다. 특히, 마이크로프로세서를 이용하거나 PC-based 제어기를 설계할 때 이러한 추론시간은 매우 중요한 문제가 된다. 본 논문에서는 이러한 추론시간을 효율적으로 줄이기 위해, 즉 추론 시 필요로 하는 곱 연산의 수를 줄이기 위하여 삼각형 소속함수를 이용하는 퍼지시스템에 적용 가능하며 정보의 손실이 발생되지 않는 간단한 고속 퍼지추론 알고리즘을 제안한다. 이것은 퍼지추론 시 입력상태공간의 분할과 간단한 기하학적 해석을 통해 얻어지는 것이며 결과적으로 퍼지규칙의 수를 줄이는 것과 같다. Almost applications using fuzzy theory are based on the fuzzy inference. However fuzzy inference needs much time in calculation process for the fuzzy system with many input variables or many fuzzy labels defined on each variable. Inference time is dependent on the number of arithmetic Product in computation Process. Especially, the inference time is a primary constraint to fuzzy control applications using microprocessor or PC-based controller. In this paper, a simple fast fuzzy inference algorithm(FFIA), without loss of information, was proposed to reduce the inference time based on the fuzzy system with triangular membership functions in antecedent part of fuzzy rule. The proposed algorithm was induced by using partition of input state space and simple geometrical analysis. By using this scheme, we can take the same effect of the fuzzy rule reduction.

Realization of PID controls by fuzzy control methods

This paper shows that PID controllers can be realized by fuzzy control methods of “product-sum-gravity method” and “simplified fuzzy reasoning method”. PID controllers, however, cannot be constructed by min-max-gravity method known as Mamdani's fuzzy reasoning method. Furthermore, extrapolative reasoning can be executed by the product-sum-gravity method and simplified fuzzy reasoning method by extending membership functions of antecedent parts of fuzzy rules.