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Computer aided systems have been frequently used in recent times and the evolution of artificial intelligence allows the inclusion of this kind of systems in most complex problems, for example in diagnosis problems. Some of the most powerful algorithms or methods that have been applied to diagnosis problems are: Artificial Neural Networks, Support Vector Machines, Decision Threes, Fuzzy Inference Systems and hybrids of these algorithms. The present paper explores the applications of a special approximation of the Type-2 Fuzzy Inference System, which is the Shadowed Type-2 Fuzzy Inference System, and the reason for using this approach (and not another one) is because it provides a good approximation to General Type-2 Fuzzy Inference Systems, but with a computational cost reduction. Shadowed Type-2 Fuzzy Inference Systems are inspired on the Shadows Sets that simplify the traditional Type-1 Membership Function using two optimal α -cuts, which in our case are applied in the secondary membership function of the General Type-2 Fuzzy Inference System (modeled by the α -planes representation) in order to model the system with only two α -planes. Experiments were realized with eleven benchmark datasets in order to evaluate the accuracy of the proposed system. The obtained results demonstrate the advantages of using this approach over the conventional General Type-2 Fuzzy Inference Systems, and this is because we obtain better performance in most of the cases and with less computational resources. [ABSTRACT FROM AUTHOR]

Neuro-fuzzy systems have received considerable attention from academia in the last decade. They can provide a tradeoff between the performance of artificial neural networks and the interpretability of fuzzy inference systems expressed through fuzzy rules. Single techniques do not always provide the optimal performance, especially when the results are susceptible to changes in the data or hyperparameters. Therefore, ensemble learning can be used to build a more stable and robust model. In this paper, a systematic literature review of studies evaluating neuro-fuzzy ensembles was performed to highlight the importance of ensemble learning and its role in improving the performance of neuro-fuzzy systems. Many aspects are highlighted, including publication years, sources, contribution types, application domains, single neuro-fuzzy systems, ensemble techniques, and the overall performance and interpretability. A total of forty-eight articles published from 2000 to March 2022 addressing the use of neuro-fuzzy ensembles in different engineering applications were selected and analyzed. Results show that: (i) Takagi–Sugeno–Kang neuro-fuzzy systems are the most evaluated category, especially the ANFIS single. (ii) Homogeneous​ ensembles, in particular boosting, are the most investigated. (iii) Logical, relational and ANFIS ensembles are outperforming. And (iv) The interpretability of neuro-fuzzy ensembles was not thoroughly investigated, but based on the number of rules, logical systems were the best. [ABSTRACT FROM AUTHOR]

The huge complexity and uncertainty in real life requires the use of advanced automatic learning methods to find out better approximators and suitable relationship in real data behavior. Neuro fuzzy systems have been proved to be excellent universal approximators. In this paper we propose a new based function Interval Type-2 Fuzzy Neural Network denoted ”Beta basis function Interval Type-2 Fuzzy Neural Network”, the BIT2FNN. The main idea is to involve type-2 beta fuzzy sets in the design process of fuzzy networks. The proposed architecture is based on beta type-2 fuzzy sets in the antecedent part, while the consequent part achieves the TSK (Takagi–Sugeno–Kang) fuzzy output strategy. Thanks to the beta function flexibility, the network achieve a good performance and shows a good resistance to noisy data. First order derivatives of type-1 and type-2 Beta functions were developed for the first time for designing fuzzy logic systems based on given input–output pairs. The backpropagation algorithm was used for the learning process of antecedent fuzzy beta parameters and the consequent part. The performance of the proposed model of Beta fuzzy logic system is evaluated with mainly two problems of time series applications : the Mackey Glass Chaotic Time-Series prediction problem with different setting of parameters and levels of noise and the ECG heart-rate Time Series monitoring problem. [ABSTRACT FROM AUTHOR]

The pneumatic muscle actuator (PMA) is one of the most promising actuators especially for the applications that require greater proximity between the humans and the robots. Fast and precise control of PMA, however, is difficult to achieve due to the compressibility of the air and the elasticity of the PMA. In order to achieve accurate and consistent tracking performance of a one axis PMA actuated manipulator over considerably wide ranges of frequency and stroke, an intelligent adaptive control algorithm is developed in this paper. The adaptive learning is enabled by a neural network in which the control gains to a fuzzy sliding mode controller (FSMC) and an integrator are adjusted to minimize the tracking error. Experimental results show that the proposed control strategy achieves fast, accurate and consistent performance tracking sinusoidal reference trajectories up to 1 Hz in frequency and close to the extreme stroke of the PMA actuated manipulator with the compressed air regulated to 4 bar. Results also show that the proposed control strategy, with a more aggressive learning for the control gain to the FSMC, achieves satisfactory performance tracking a trapezoidal reference trajectory. [ABSTRACT FROM AUTHOR]

This paper proposes a method and an algorithm to implement interpretable fuzzy reinforcement learning (IFRL). It provides alternative solutions to common problems in RL, like function approximation and continuous action space. The learning process resembles that of human beings by clustering the encountered states, developing experiences for each of the typical cases, and making decisions fuzzily. The learned policy can be expressed as human-intelligible IF-THEN rules, which facilitates further investigation and improvement. It adopts the actor–critic architecture whereas being different from mainstream policy gradient methods. The value function is approximated through the fuzzy system AnYa. The state–action space is discretized into a static grid with nodes. Each node is treated as one prototype and corresponds to one fuzzy rule, with the value of the node being the consequent. Values of consequents are updated using the Sarsa(λ) algorithm. Probability distribution of optimal actions regarding different states is estimated through Empirical Data Analytics (EDA), Autonomous Learning Multi-Model Systems (ALMMo), and Empirical Fuzzy Sets (ε FS). The fuzzy kernel of IFRL avoids the lack of interpretability in other methods based on neural networks. Simulation results with four problems, namely Mountain Car, Continuous Gridworld, Pendulum Position, and Tank Level Control, are presented as a proof of the proposed concept. [ABSTRACT FROM AUTHOR]