This paper presents and investigates a neural network structure which can perform general fuzzy inference. This system consists of a number of parallel neural network units which are called ''flexible inferenc...
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This paper presents and investigates a neural network structure which can perform general fuzzy inference. This system consists of a number of parallel neural network units which are called ''flexible inference cells'' (FICs). Each FIC implements a single-input/single-output (SISO) IF-THEN rule of a fuzzy knowledge base. The assumption of SISO fuzzy rules allows the implementation of any complex fuzzy inference algorithm (for control or other decision making purposes), since any MIMO (multi-input/multi-output) rule can be decomposed into an equivalent set of MISO (multi-input/single-output) rules, and a MISO rule can be decomposed to a set of SISO rules. The paper discusses the assumptions and requirements for the proposed neurofuzzy structure, and classifies the FICs into four categories. Some results derived by simulation using 3125 exemplar patterns produced computationally are provided.
Neural networks or connectionist models are massively parallel interconnections of simple neurons that work as a collective system, can emulate human performance and provide high computation rates. On the other hand, ...
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Neural networks or connectionist models are massively parallel interconnections of simple neurons that work as a collective system, can emulate human performance and provide high computation rates. On the other hand, fuzzy systems are capable to model uncertain or ambiguous situations that are so often encountered in real life. One way for implementing fuzzy systems is through utilizations of the expert system architecture. Recently, many attempts have been made to ''fuse'' fuzzy systems and neural nets in order to achieve better performance in reasoning and decision making processes. The systems that result from such a fusion are called neuro-fuzzy inference systems and possess combined features. The purpose of the present paper is to propose such a neuro-fuzzy system by extending and improving the system of Keller et al. (1992). The present system makes use of Hamacher's fuzzy intersection function and Sugeno's complement function. After a brief outline of the operation of the system its features are established with the aid of four theorems which are fully proved. The capabilities of the system are shown by a set of simulation results derived for the case of trapezoidal fuzzy sets. These results are shown to be better than the ones obtained with the original neuro-fuzzy system of Keller et al.
The problem of scheduling n dependent tasks, with arbitrary processing times, on m identical machines so as to minimize the makespan criterion is considered. Since this problem is NP-hard in the strong sense, it can b...
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The problem of scheduling n dependent tasks, with arbitrary processing times, on m identical machines so as to minimize the makespan criterion is considered. Since this problem is NP-hard in the strong sense, it can be solved only suboptimally using heuristic approaches. Two new heuristic algorithms (dispatching rules), namely MVT/MISF acid DMVT/MISF algorithms, for this problem are proposed. These algorithms are then used, together with the existing ones CP/MISF and DHLF/MISF, as a dispatching rule base of a new adaptively weighted combinatorial dispatching (AWCD) rule. This combinatorial dispatching rule has a superior behaviour compared to simple dispatching rules. Extended experimentation with these algorithms supports this argument. Here a representative robotic dynamics computation example is included. In addition, some empirical rules are derived and proposed for the selection of a simple dispatching rule (heuristic) if such a selection is required, for each particular input data set. These methods, as well as the existing optimal algorithms for special solvable cases of the considered problem, have been integrated in a decision support system (DSS).
Potential field-based collision avoidance algorithms for mobile robots frequently assume vehicles and obstacles to have circular or spherical shapes. This assumption not only simplifies the analysis but also limits th...
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In this paper, we propose a control scheme to ensure the microgrid control layers are resilient to cyberattacks. The studied microgrid consists of several distributed generation (DG) units and we consider the hierarch...
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Given a linear, time-invariant, discrete-time plant, we consider the optimal control problem of minimizing, by choice of a stabilizing compensator, the seminorm of a selected closed-loop map in a basic feedback system...
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Given a linear, time-invariant, discrete-time plant, we consider the optimal control problem of minimizing, by choice of a stabilizing compensator, the seminorm of a selected closed-loop map in a basic feedback system. The seminorm can be selected to reflect any chosen performance feature and must satisfy only a mild condition concerning finite impulse responses. We show that if the plant has no poles or zeros on the unit circle, then the calculation of the minimum achievable seminorm is equivalent to the maximization of a linear objective over a convex set in a low-dimensional Euclidean space. Hence, for a wide variety of optimal control problems, one can compute the answer to an infinite-dimensional optimization by a finite-dimensional procedure. This allows the use of effective numerical methods for computation.
In this paper, we propose a method of designing the sampled-data tracking controller for nonlinear systems expressed by the Takagi-Sugeno (T-S) fuzzy model. A sufficient condition that asymptotically stabilizes the st...
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The research presented in this article aims to determine the possibility of identifying macular pathologies from optical coherence tomography (OCT) images. Early detection of a pathology in the human eye retina is ben...
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Five scheduling problems are considered, concerning unit-length independent tasks and uniform machines. New improved optimal algorithms are presented that can solve these problems in at most O(n log n) time, where n i...
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Five scheduling problems are considered, concerning unit-length independent tasks and uniform machines. New improved optimal algorithms are presented that can solve these problems in at most O(n log n) time, where n is the number of tasks. The existing algorithms solve most of these problems in O(n3) time. Proofs of optimality of the present algorithms are included, and simple representative examples are provided that illustrate the type of results obtained.
In robotics, control barrier function (CBF)–based safety filters are commonly used to enforce state constraints. A critical challenge arises when the relative degree of the CBF varies across the state space. This var...
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