Session Program

Abstract - In this paper, we put forward an interval type-2 fuzzy neural network (IT2FNN) to deal with control issues of nonlinear systems with uncertainties. The fuzzy rules of the IT2FNN use interval type-2 triangular fuzzy sets to account for antecedent parts and adopt crisp numbers for the corresponding consequents. To effectively cope with uncertainties in the systems, a sliding-mode-control theory-based approach with new parameter learning rules is proposed to update the IT2FNN. The overall stability of the proposed methodology is also proved by using appropriate Lyapunov functions. Finally, the proposed method is applied to control the angular position of an inverted pendulum system. Simulation results indicate that, compared to a conventional proportional-derivative controller, the IT2FNN with the proposed learning rules can eliminate the uncertainties in performance and efficiently track the angle trajectory as desired.

Abstract - Learning of fuzzy parameters for system modeling using evolutionary algorithms is an interesting topic. In this paper, two optimal design and tuning of Interval type-2 fuzzy logic system are proposed using hybrid learning algorithms. The consequent parameters of the interval type-2 fuzzy logic system in both the hybrid algorithms are tuned using Kalman filter. Whereas the antecedent parameters of the system in the first hybrid algorithm is optimized using the multi-objective particle swarm optimization (MOPSO) and using the multi-objective evolutionary algorithm Based on Decomposition (MOEA/D) in the second hybrid algorithm. Root mean square error and maximum absolute error as the two accuracy objective are utilized to find the Pareto-optimal solution with the MOPSO and MOEA/D respectively. The proposed hybrid multi-objective designs of the interval type-2 fuzzy logic system are utilized to the prediction of solar photovoltaic output. It is observed that MOEA/D outperforms MOPSO in this case in terms of quality of results and its diversity. Finally, one point is selected from the obtained Pareto front and its performance is illustrated.

Abstract - In the paper, Z number based neuro-fuzzy network (ZNFN) for control of dynamic plants is presented. Using interpolative reasoning mechanism the structure and design algorithms of ZNFN are proposed. The gradient learning algorithm is designed to update the parameters of ZNFN. The proposed ZNFN structure is tested for control of dynamic plants and the performance of ZNFN based control system is compared with the performance of the other controllers. The obtained results demonstrate the suitability of using of designed system in control of dynamic plants

Abstract - The Random-Weight Fuzzy Neural Network is an inference system where the fuzzy rule parameters of antecedents (i.e., membership functions) are randomly generated and the ones of consequents are estimated using a Regularized Least Squares algorithm. In this regard, we propose an on-line learning algorithm under the hypothesis of training data distributed across a network of interconnected agents. In particular, we assume that each agent in the network receives a stream of data as a sequence of mini-batches. When receiving a new chunk of data, each agent updates its estimate of the consequent parameters and, periodically, all agents agree on a common model through the Distributed Average Consensus protocol. The learning algorithm is faster than a solution based on a centralized training set and it does not rely on any coordination authority. The experimental results on well-known datasets validate our proposal.

Abstract - Electricity price forecasting is considered as an important tool for energy-related utilities and power generation industries. The deregulation of power market, as well as the competitive financial environment, which have introduced new market players in this field, makes the electricity price forecasting problem a demanding mission. The main focus of this paper is to investigate the performance of asymmetric neuro-fuzzy network models for day-ahead electricity price forecasting. The proposed model has been developed from existing Takagi-Sugeno-Kang fuzzy systems by substituting the IF part of fuzzy rules with an asymmetric Gaussian function. In addition, a clustering method is utilised as a pre-processing scheme to identify the initial set and adequate number of clusters and eventually the number of rules in the proposed model. The results corresponding to the minimum and maximum electricity price have indicated that the proposed forecasting scheme could be considered as an improved tool for the forecasting accuracy.

Abstract - A significant number of investigations of type-1 and type-2 fuzzy logic controllers have revealed their exceptional ability to capture uncertainties in complex and nonlinear systems, particularly in real-time control applications. However, regardless of being type-1 or type-2, fuzzy logic controller design is still a complicated task due to the lack of a closed form solution of the output and an interpretable relationship between the control output and fuzzy logic controller design parameters, such as center or width of the membership functions. To simplify the design procedure further, we think every attempt to obtain such interpretable relationships is worthwhile. Accordingly, this paper aims to design a double-input interval type-2 fuzzy PID controller and obtain interpretable relationships between the input and the output of the controller. Thereafter, we deploy the novel design for the control of a Y6 coaxial tricopter unmanned aerial vehicle. Simulation results, which are realised in robot operating system (ROS) using C++ and Gazebo environment, are found to tally with the theoretical analysis and claims in the paper.