In this paper, a novel 1-norm extreme learning machine (ELM) for regression and multiclass classification is proposed as a linear programming problem whose solution is obtained by solving its dual exterior penalty pro...
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In this paper, a novel 1-norm extreme learning machine (ELM) for regression and multiclass classification is proposed as a linear programming problem whose solution is obtained by solving its dual exterior penalty problem as an unconstrained minimization problem using a fast Newton method. The algorithm converges from any starting point and can be easily implemented in MATLAB. The main advantage of the proposed approach is that it leads to a sparse model representation meaning that many components of the optimal solution vector will become zero and therefore the decision function can be determined using much less number of hidden nodes in comparison to ELM. Numerical experiments were performed on a number of interesting real-world benchmark datasets and their results are compared with ELM using additive and radial basis function (RBE) hidden nodes, optimally pruned ELM (OP-ELM) and support vector machine (SVM) methods. Similar or better generalization performance of the proposed method on the test data over ELM, OP-ELM and SVM clearly illustrates its applicability and usefulness. (C) 2013 Elsevier B.V. All rights reserved.
This paper shows that the minimum ratio canceling algorithm of Wallacher (Unpublished manuscript, Institut fur Ange-wandte Mathematik, Technische Universitat, Braunschweig (1989)) (and a faster relaxed version) can be...
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This paper shows that the minimum ratio canceling algorithm of Wallacher (Unpublished manuscript, Institut fur Ange-wandte Mathematik, Technische Universitat, Braunschweig (1989)) (and a faster relaxed version) can be generalized to an algorithm for general linear programs with geometric convergence. This implies that when we have a negative cycle oracle, this algorithm will compute an optimal solution in (weakly) polynomial time. We then specialize the algorithm to linearprogramming on unimodular linear spaces, and to the minimum cost flow and (dual) tension problems. We construct instances proving that even in the network special cases the algorithm is not strongly polynomial. (C) 2000 Elsevier Science B.V. All rights reserved.
It is shown that the holes of the objects in an input image with a CT-CNN [1] or a DT-CNN [2] may be obtained in a single transient using just one linear parameter configuration. A set of local rules is given that des...
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It is shown that the holes of the objects in an input image with a CT-CNN [1] or a DT-CNN [2] may be obtained in a single transient using just one linear parameter configuration. A set of local rules is given that describe how a CNN with a linear configuration may extract the hole of the objects of an input image in a single transient. The parameter configuration for DT-CNNs or for CT-CNNs is obtained as the solution of a single linear programming problem, including robustness as an objective. The tolerances to multiplicative and additive errors caused by circuit inaccuracies for the linear hole-extraction configurations proposed have been deduced. These tolerable errors have been corroborated by simulations. The tolerance to errors and the speed of the CT-CNN linear configuration proposed for hole extraction are compared with those of the CT-CNN nonlinear configuration found in the bibliography [3].
Conditional specification of distributions is a developing area with increasing applications. In the finite discrete case, a variety of compatible conditions can be derived. In this paper, we propose an alternative ap...
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Conditional specification of distributions is a developing area with increasing applications. In the finite discrete case, a variety of compatible conditions can be derived. In this paper, we propose an alternative approach to study the compatibility of two conditional probability distributions under the finite discrete setup. A technique based on rank-based criterion is shown to be particularly convenient for identifying compatible distributions corresponding to complete conditional specification including the case with *** proposed methods are illustrated with several examples.
Overcurrent relays (OCR) are the major protection devices in a distribution system. To reduce the power outages, mal-operation of the backup relays should be avoided, and therefore. OCR time coordination in power dist...
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Overcurrent relays (OCR) are the major protection devices in a distribution system. To reduce the power outages, mal-operation of the backup relays should be avoided, and therefore. OCR time coordination in power distribution network is a major concern of protection engineer. The OCR time coordination in ring fed distribution networks is a highly constrained optimization problem. The purpose is to find an optimum relay setting to minimize the time of operation of relays and at the same time, to avoid the mal-operation of relays. This paper presents continuous genetic algorithm (CGA) technique for optimum coordination of OCR in a ring fed distribution system. Constraints are incorporated in the fitness function making use of the penalty method. The CGA is inherently faster than binary genetic algorithm (GA) because the chromosomes do not have to be decoded. Also the CGA gives an advantage of requiring less storage than binary GA. (C) 2011 Elsevier Ltd. All rights reserved.
In this paper we introduce the concept of s-monotone index selection rule for linear programming problems. We show that several known anti-cycling pivot rules like the minimal index, Last-In-First-Out and the most-oft...
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In this paper we introduce the concept of s-monotone index selection rule for linear programming problems. We show that several known anti-cycling pivot rules like the minimal index, Last-In-First-Out and the most-often-selected-variable pivot rules are s-monotone index selection rules. Furthermore, we show a possible way to define new s-monotone pivot rules. We prove that several known algorithms like the primal (dual) simplex, MBU-simplex algorithms and criss-cross algorithm with s-monotone pivot rules are finite methods. We implemented primal simplex and primal MBU-simplex algorithms, in MATLAB, using three s-monotone index selection rules, the minimal-index, the Last-In-First-Out (LIFO) and the Most-Often-Selected-Variable (MOSV) index selection rule. Numerical results demonstrate the viability of the above listed s-monotone index selection rules in the framework of pivot algorithms (c) 2012 Elsevier B.V. All rights reserved.
The augmented Lagrangian and Newton methods are used to simultaneously solve the primal and dual linear programming problems. The proposed approach is applied to the primal linear programming problem with a very large...
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The augmented Lagrangian and Newton methods are used to simultaneously solve the primal and dual linear programming problems. The proposed approach is applied to the primal linear programming problem with a very large number (approximate to 10(6)) of nonnegative variables and a moderate (approximate to 10(3)) number of equality-type constraints. Computation results such as the solution of a linear programme with 10 million primal variables are presented.
In this letter we present an electronic circuit based on a neural net to compute the discrete Walsh transform. We show both analytically and by simulation that the circuit is guaranteed to settle into the correct values.
In this letter we present an electronic circuit based on a neural net to compute the discrete Walsh transform. We show both analytically and by simulation that the circuit is guaranteed to settle into the correct values.
This study addresses the stabilisation problem via decentralised controllers for continuous-time positive fractional-order (FO) interconnected systems. By virtue of the positivity, a necessary and sufficient condition...
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This study addresses the stabilisation problem via decentralised controllers for continuous-time positive fractional-order (FO) interconnected systems. By virtue of the positivity, a necessary and sufficient condition is first derived for asymptotic stability of the interconnected closed-loop system. Then, necessary and sufficient conditions for designing a decentralised state-feedback controller are formulated in the form of a linear programming problem which can be effectively solved by using various convex optimisation algorithms. The effectiveness of the obtained theoretical results is demonstrated by a numerical example in positive FO circuits.
Neutrosophic Set (NS) allows us to handle uncertainty and indeterminacy of the data. Several researchers have investigated the Transportation problems (TP) with various forms of input data. This paper emphasizes a dyn...
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Neutrosophic Set (NS) allows us to handle uncertainty and indeterminacy of the data. Several researchers have investigated the Transportation problems (TP) with various forms of input data. This paper emphasizes a dynamic optimal solution framework for TPs in a neutrosophic setting. This paper investigates a Neutrosophic Transportation problem (NTP) in which supply, demand, and transportation cost are considered as Single-Valued Neutrosophic Trapezoidal Numbers (SVNTrNs). The weighted possibilistic mean value of their truth, indeterminacy, and facility membership function are calculated. Then, NTP is modelled as a parametric linear programming problem (LPP) and solved. Further, the drawbacks of the existing approaches and advantages of the developed method are discussed. Finally, the real-time problem and numerical illustrations are presented and compared to existing solutions. This study helps the Decision-Makers (DMs) in budgeting their transportation expenses through strategic distribution.
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