In this paper we introduce a fuzzy uncertainty assessment methodology based on Neutrosophic Sets (NS). This is achieved via the implementation of a Radial Basis Function Neural-Network (RBF-NN) for multiclass classifi...
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In this paper we introduce a fuzzy uncertainty assessment methodology based on Neutrosophic Sets (NS). This is achieved via the implementation of a Radial Basis Function Neural-Network (RBF-NN) for multiclass classification that is functionally equivalent to a class of Fuzzy Logic systems (FLS). Two types of uncertainties are considered: a) fuzziness and b) ambiguity, with both uncertainty types measured in each receptive unit (RU) of the hidden layer of the RBF-NN. The use of NS assists in the quantification of the uncertainty and formation of the rulebase;the resulting RBF-NN modelling structure proves to have enhanced transparency features to interpretation that enables us to understand the influence of each system parameter thorughout the parameter identification. The presented methodology is based on firstly constructing a neutrosophic set by calculating the associated fuzziness in each rule - and then use this information to train the RBF-NN;and secondly, an ambiguity measure that is defined via the truth and falsity measures related to each normalised consequence of the fuzzy rules within the RUs. In order to evaluate the individual ambiguity in the RUs and then the average ambiguity of the whole system, a neutrosophic set is constructed. Finally, the proposed methodology is tested against two case studies: a benchmark dataset problem and a real industrial case study. On both cases we demonstrate the effectiveness of the developed methodology in automatically creating uncertainty measures and utilising this new information to improve the quality of the trained model.
In this paper we consider constrained ℓ 0 sparse optimization problems, that is, constrained problems with the objective function composed of a smooth part and an ℓ 0 regularization term. We analyze a penalty decomp...
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In this paper we consider constrained ℓ 0 sparse optimization problems, that is, constrained problems with the objective function composed of a smooth part and an ℓ 0 regularization term. We analyze a penalty decomposition (PD) method for solving these nonconvex problems, in which a sequence of penalty subproblems are solved by alternating minimization (AM) method. Although the (AM) method finds only a local solution of the subproblem, the sequence generated by (PD) algorithm converges to a local minimum of the original problem. We estimate the iteration complexity of the (AM) method used for finding a local minimum of the penalty subproblem. In particular we prove that, under strong convexity assumption, this method has linear convergence. As an application for our general model, we propose the ℓ 0 trend filtering for estimation of the mean and variance of a given time series. We test the practical performance of our (PD) algorithm on such ℓ 0 trend filtering problems.
For PD pattern recognition, six kinds of typical cable fault models are made and tested. Characteristics are calculated from test data. Multiple statistical characteristics of PD distribution map are calculated and pa...
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Most autonomous robotic agents use logic inference to keep to safe and permitted behaviour. Given a set of rules, it is important that the robot is able to establish the consistency of its rules and its current percep...
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Most autonomous robotic agents use logic inference to keep to safe and permitted behaviour. Given a set of rules, it is important that the robot is able to establish the consistency of its rules and its current perception-based beliefs. This paper investigates how a robotic agent can use model checking to examine the consistency of its rules and beliefs. A rule set is modelled by a Boolean evolution system with synchronous semantics which can be translated into a labelled transition system (LTS). It is proven that stability and consistency can be formulated as computation tree logic (CTL) and linear temporal logic (LTL) properties. Two new algorithms are presented to perform realtime consistency and stability checks respectively, which is crucial for efficient consistency checks by robots.
We give explicit analytic formulas for computing the L 2 norm of a discrete-time generalised system whose rational transfer matrix function may be improper or polynomial. The norm is expressed in terms of solutions o...
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We give explicit analytic formulas for computing the L 2 norm of a discrete-time generalised system whose rational transfer matrix function may be improper or polynomial. The norm is expressed in terms of solutions of generalized Lyapunov equations, written down with coefficients from a special type of realisation of the underlying transfer function matrix, much in the same spirit of the standard (proper) case. The main result hints to a numerically-sound prototype algorithm that relies on standard reliable software for computing solutions of generalised Lyapunov equations.
This paper presents the development of an improved spiral dynamic optimisation algorithm with application to fuzzy logic dynamic modelling of a twin rotor system. Spiral motion and dynamic step size generated by a spi...
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This paper presents the development of an improved spiral dynamic optimisation algorithm with application to fuzzy logic dynamic modelling of a twin rotor system. Spiral motion and dynamic step size generated by a spiral model produce unique exploration and exploitation strategies of a spiral dynamic algorithm. However, the algorithm is subject to settling into local optima at the end of the search process due to insufficient exploration throughout the search area. An elimination and dispersal strategy of a bacterial foraging algorithm is adopted to solve the problem. Moreover, the application of acquiring nonlinear dynamic model of a helicopter model prototype in hovering mode is presented and the results show the effectiveness of the proposed algorithm to solve real world problems. The result of the modelling work is presented in both time-domain and frequency-domain. It shows that the fuzzy model optimized by the proposed algorithm is better and adequate to represent the characteristic behaviour of the helicopter model prototype system.
In the last years, nonparametric linear dynamical systems modeling has regained attention in the system identification world. In particular, the application of regularization techniques that were already widely used i...
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This paper focuses on tracking large groups of objects, such as crowds of pedestrians. Large groups generate multiple measurements with uncertain origin. Additionally, often the sensor noise characteristics are unknow...
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This paper focuses on tracking large groups of objects, such as crowds of pedestrians. Large groups generate multiple measurements with uncertain origin. Additionally, often the sensor noise characteristics are unknown but bounded within known intervals. Hence, these two types of uncertainties call for flexible techniques capable of offering a solution in the presence of data association and also to cope with the presence of nonlinearities. This paper presents a box particle filter for large crowds tracking able to deal with such challenges. The filter measurement update step is performed by solving a dynamic constraint satisfaction problem (DSCP) with the multiple measurements. The box particle filter performance is validated over a realistic scenario comprising a large crowd of pedestrians. Promising results are presented in terms of accuracy and computational complexity.
In this paper we investigate the problem of optimal real-time power dispatch of an interconnection of conventional power generation plants, renewable resources and energy storage systems. The objective is to minimize ...
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