Fuzzy inference systems (FIS) are widely used for process simulation or control. They can be designed either from expert knowledge or from data. For complex systems, FIS based on expert knowledge only may suffer from a loss of accuracy. This is the main incentive for using fuzzy rules inferred from data. Designing a FIS from data can be decomposed into two main phases: automatic rule generation and system optimization. Rule generation leads to a basic system with a given space partitioning and the corresponding set of rules. System optimization can be done at various levels. Variable selection can be an overall selection or it can be managed rule by rule. Rule base optimization aims to select the most useful rules and to optimize rule conclusions. Space partitioning can be improved by adding or removing fuzzy sets and by tuning membership function parameters. Structure optimization is of a major importance: selecting variables, reducing the rule base and optimizing the number of fuzzy sets. Over the years, many methods have become available for designing FIS from data. Their efficiency is usually characterized by a numerical performance index. However, for human-computer cooperation another criterion is needed: the rule interpretability. An implicit assumption states that fuzzy rules are by nature easy to be interpreted. This could be wrong when dealing with complex multivariable systems or when the generated partitioning is meaningless for experts. The paper analyzes the main methods for automatic rule generation and structure optimization. They are grouped into several families and compared according to the rule interpretability criterion. For this purpose, three conditions for a set of rules to be interpretable are defined.

/pdf/designing-fuzzy-inference-systems-from-data-an-prp02b4w15.pdf

Designing fuzzy inference systems from data: An interpretability-oriented review

Remote laboratories have been introduced during the last few decades into engineering education processes as well as integrated within e-learning frameworks offered to engineering and science students. Remote laboratories are also being used to support life-long learning and student's autonomous learning activities. In this paper, after a brief overview of state-of-the-art technologies in the development of remote laboratories and presentation of recent and interesting examples of remote laboratories in several areas related with industrial electronics education, some current trends and challenges are also identified and discussed.

Current Trends in Remote Laboratories

This paper proposes a systematic methodology of fuzzy logic modeling for complex system modeling. It has a unified parameterized reasoning formulation, an improved fuzzy clustering algorithm, and an efficient strategy of selecting significant system inputs and their membership functions. The reasoning mechanism introduces 4 parameters whose variation provides a continuous range of inference operation. As a result, we are no longer restricted to standard extremes in any step of reasoning. The fuzzy model itself can then adjust the reasoning process by optimizing the inference parameters based on input-output data. The fuzzy rules are generated through fuzzy c-means (FCM) clustering. Major bottlenecks are addressed and analytical solutions are suggested. We also address the classification process to extend the derived fuzzy partition to the entire output space. In order to select suitable input variables among a finite number of candidates (unlike traditional approaches) we suggest a new strategy through which dominant input parameters are assigned in one step and no iteration process is required. Furthermore, a clustering technique called fuzzy fine clustering is introduced to assign the input membership functions. In order to evaluate the proposed methodology, two examples-a nonlinear function and a gas furnace dynamic procedure-are investigated in detail. The significant improvement of the model is concluded compared to other fuzzy modeling approaches.

Development of a systematic methodology of fuzzy logic modeling

To design a fuzzy rule-based classification system (fuzzy classifier) with good generalization ability in a high dimensional feature space has been an active research topic for a long time. As a powerful machine learning approach for pattern recognition problems, the support vector machine (SVM) is known to have good generalization ability. More importantly, an SVM can work very well on a high- (or even infinite) dimensional feature space. This paper investigates the connection between fuzzy classifiers and kernel machines, establishes a link between fuzzy rules and kernels, and proposes a learning algorithm for fuzzy classifiers. We first show that a fuzzy classifier implicitly defines a translation invariant kernel under the assumption that all membership functions associated with the same input variable are generated from location transformation of a reference function. Fuzzy inference on the IF-part of a fuzzy rule can be viewed as evaluating the kernel function. The kernel function is then proven to be a Mercer kernel if the reference functions meet a certain spectral requirement. The corresponding fuzzy classifier is named positive definite fuzzy classifier (PDFC). A PDFC can be built from the given training samples based on a support vector learning approach with the IF-part fuzzy rules given by the support vectors. Since the learning process minimizes an upper bound on the expected risk (expected prediction error) instead of the empirical risk (training error), the resulting PDFC usually has good generalization. Moreover, because of the sparsity properties of the SVMs, the number of fuzzy rules is irrelevant to the dimension of input space. In this sense, we avoid the "curse of dimensionality." Finally, PDFCs with different reference functions are constructed using the support vector learning approach. The performance of the PDFCs is illustrated by extensive experimental results. Comparisons with other methods are also provided.

/pdf/support-vector-learning-for-fuzzy-rule-based-classification-39ehpd148j.pdf

Support vector learning for fuzzy rule-based classification systems

This paper proposes an adaptive fuzzy sliding mode controller for robotic manipulators. An adaptive single-input single-output (SISO) fuzzy system is applied to calculate each element of the control gain vector in a sliding mode controller. The adaptive law is designed based on the Lyapunov method. Mathematical proof for the stability and the convergence of the system is presented. Various operation situations such as the set point control and the trajectory control are simulated. The simulation results demonstrate that the chattering and the steady state errors, which usually occur in the classical sliding mode control, are eliminated and satisfactory trajectory tracking is achieved.

An adaptive fuzzy sliding mode controller for robotic manipulators

A unified parameterized formulation of reasoning in fuzzy modeling and control

Hardware-in-the-loop simulation is an increasingly popular engineering tool for its effectiveness in maintaining a balance between the two competing demands of: a) well designed and thoroughly tested systems, and b) reduced development time and costs to remain competitive. This paper proposes an architecture for applying hardware-in-the-loop simulation techniques to the design and testing of robot manipulators. Potential benefits of this architecture include allowing concurrent development of hardware and control system components and providing a reusable platform for reconfigurable manipulators through a generic and modular structure. An implementation of this architecture in the preliminary phases of development is presented here to demonstrate how these benefits can be realized.

An Architecture for Robotic Hardware-in-the-Loop Simulation

Operation of reservoirs and power plants for better management of water resources and production of hydro-electric energy has been the objective of many studies. In this research, shark algorithm is used for management of water resources and hydro-electric plants. After the introduction of this procedure, the algorithm is applied to some complex cases such as Karun-4 reservoir, 4-reservoir system, 10-reservoir system and another one including 26 power plants. In the Karun-4 case, the aim was to reduce water shortages and the results obtained from shark algorithm were in 100% compliance with the absolute optimum answer obtained from Lingo software and non-linear method. This was the best solution to the problem to date in the published researches. In the 4-reservoir system, the objective was to increase the profit from the reservoirs. The shark algorithm yielded a value of 1194.64, which is the best answer to date to the question. In regard to the energy production by the 26 power plants, the shark algorithm yielded 40% more energy, compared to genetic algorithm.

Optimization of Chain-Reservoirs’ Operation with a New Approach in Artificial Intelligence

A robotic hardware-in-the-loop simulation platform was designed to aid in the synthesis and analysis of robot manipulators and their controllers. The platform uses a load emulation mechanism to apply nonlinear and coupled dynamic loads to the joint hardware. Some experimental results from a 5-DOF industrial manipulator are provided, demonstrating how the platform can simulate the performance of a manipulator under normal and aggressive operating conditions.

M.R. Emami

Papers

Development of a systematic methodology of fuzzy logic modeling

A unified parameterized formulation of reasoning in fuzzy modeling and control

An Architecture for Robotic Hardware-in-the-Loop Simulation

Optimization of Chain-Reservoirs’ Operation with a New Approach in Artificial Intelligence

Dynamic Load Emulation in Hardware-in-the-Loop Simulation of Robot Manipulators