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DEX (Decision EXpert) is a qualitative multi-criteria decision-modelling method in which decision alternatives are evaluated according to decision rules, elicited from individual decision makers. In this preliminary study, we assessed the differences between decision rules acquired from different subjects (inter-personal differences) and from the same subjects at different times (intra-personal differences). We also assessed the consistency of soacquired rules and the ability of subjects to estimate the importance (weights) of criteria. The results indicate a high variability of decision rules, both interand intra-personal. Intra-personal drift is lower than inter-personal differences, but not substantially. The consistency of rules varied between a small decision table with clearly ordered criteria, where it was almost perfect, and a large decision table with less apparent preferential relations, where it was rather poor at the average level of 0.77. Criteria weighs also drifted at the rate 9-19% per month. [ABSTRACT FROM AUTHOR]

In this note, we propose a new class of orthogonal polynomials (named Bachok-Hasham polynomials of the first and second kind for order k, denote it as Zk(i,n)(x), i={1,2}, which is extension of the Chebyshev polynomials of the first and second kind respectively. It is found that Bachok--Hasham polynomials of first and second kind Zk(i,n)(x) are orthogonal with respect to weights w(1,k)(x)=xk-1/√1-x2k, w(2,k)(x)=xk-1√1-x2kon the interval [-1,1], where k is positive odd integers. Spectral properties Bachok--Hasham polynomials of the first and second kind Zk(i,n)(x),i={1,2} are proved. These properties are used to solve a special class of singular integral equations. Finally, numerical examples and comparison results with other methods are provided to illustrate the effectiveness and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

The linear regression model assumes that all random error components are identically and independently distributed with constant variance. Hence, each data point provides equally precise information about the deterministic part of the total variation. In other words, the standard deviations of the error terms are constant over all values of the predictor variables. When the assumption of constant variance is violated, the ordinary least squares estimator of regression coefficient lost its property of minimum variance in the class of linear and unbiased estimators. Weighted least squares estimation are often used to maximize the efficiency of parameter estimation. A procedure that treats all of the data equally would give less precisely measured points more influence than they should have and would give highly precise points too little influence. Optimizing the weighted fitting criterion to find the parameter estimates allows the weights to determine the contribution of each observation to the final parameter estimates. This study used polynomial model with weighted least squares estimation to investigate paddy production of different paddy lots based on paddy cultivation characteristics and environmental characteristics in the area of Kedah and Perlis. The results indicated that factors affecting paddy production are mixture fertilizer application cycle, average temperature, the squared effect of average rainfall, the squared effect of pest and disease, the interaction between acreage with amount of mixture fertilizer, the interaction between paddy variety and NPK fertilizer application cycle and the interaction between pest and disease and NPK fertilizer application cycle. [ABSTRACT FROM AUTHOR]

The mathematical modelling of splicing systems (H systems) was initiated by Head in 1987. By restricting the splicing rules of splicing systems, some variants of splicing systems such as simple and semi-simple splicing systems have been developed. Due to the limitation on the generative power of the variants of splicing systems, weights have been used as the restrictions in the variants of splicing systems recently, namely weighted one-sided splicing systems, weighted simple splicing systems and weighted semi-simple splicing systems. In this paper, we investigate the generative power of weighted simple and semi-simple splicing systems by considering different and specified weighting spaces and weighting operations. In addition, the generative power of weighted simple and semi-simple splicing systems are generalised by relating their generated threshold languages to the Chomsky hierarchy. [ABSTRACT FROM AUTHOR]

A splicing system is a formal model for DNA based computation using the recombinant behavior of DNA molecules in the presence of enzymes and ligase. Since it was introduced in 1987, several variants with different restrictions and extensions have been developed. In this paper, a restricted variant of splicing systems, called one-sided splicing systems have been studied. The generative capacity of one-sided splicing systems with the presence of weight is investigated. We have also shown that the use of different weighting spaces and weight operations results in weighted one-sided splicing systems with different generative capacities. [ABSTRACT FROM AUTHOR]

A multi-criteria (MC) problem comprises of units to be analyzed under a set of evaluation criteria. Solving a MC problem is basically the process of finding the overall performance or overall quality of the units of analysis by using certain aggregation method. Based on these overall measures of each unit, a decision can be made whether to sort them, to select the best or to group them according to certain ranges. Prior to solving the MC problems, the weights of the related criteria have to be determined with the assumption that the weights represent the degree of importance or the degree of contribution towards the overall performance of the units. This paper presents two main approaches which are called as subjective and objective approaches, where the first one involves evaluator(s) while the latter approach depends on the intrinsic information contained in each criterion. The subjective and objective weights are defined if the criteria are assumed to be independent with each other, but if they are dependent, there is another type of weight, which is called as monotone measure weight or compound weights which represent degree of interaction among the criteria. The measure of individual weights or compound weights must be addressed in solving multi-criteria problems so that the solutions are more reliable since in the real world, evaluation criteria always come with different degree of importance or are dependent with each other. As the real MC problems have their own uniqueness, it is up to the decision maker(s) to decide which type of weights and which method are the most applicable ones for the problem under study. [ABSTRACT FROM AUTHOR]

Beamforming is used to achieve diversity gain. It is a technique in diversity to remedy power penalty due to channel fading. The beam-weights for multi-antenna system are evaluated for two different approaches. These weights are then used to weight the signal beams of each transmit antenna branch. Results reveal that by exploiting the transmit channel substructures exposed by the singular vector decomposition algorithm, the capacity of a multi-antenna system can be enhanced. [ABSTRACT FROM PUBLISHER]

The positioning ability is essential in mobile networks. Providing the position of entities is in general a complex task that becomes especially hard in ad hoc networks. In these networks, the topology cannot be planned and network nodes usually present restrictions in terms of computation capabilities and autonomy that constrain the network lifetime. Passive algorithms have been proposed for minimizing the impact of location services in the network, i.e. reducing the location traffic and extending the network life. However, the accuracy of passive algorithms tends to be worse than the accuracy for active approaches. This paper studies the impact of several proposals focused on improving the accuracy of the passive TDOA algorithm. Results indicate that weighing the ordinary least squares algorithm with the appropriate factors could be enough for improving the accuracy of the algorithm in some scenarios. [ABSTRACT FROM PUBLISHER]

Occupational competency can be divided into different levels according to principles of system components. Using Analytic Hierarchy Process to determine weights of factor and fuzzy evaluation method to define fuzzy judgment matrix of the influencing factor, we can gain scores backwards at all levels and components of influencing factor, which can provide judgment reason for student's occupational competency. [ABSTRACT FROM PUBLISHER]

Monitoring is a crucial factor for smooth run of distributed systems such as mobile agent based system. Various activities in such systems require monitoring such as performance analysis and tuning, scheduling strategies and fault detection. In this paper we present monitoring and fault tolerance technique for mobile agent based systems. We present mobile agent based fault prevention and detection technique where the team of mobile agents monitor each host in mobile agent based system. This research focuses on building an automatic, adaptive and predictive determining policy where critical host agents are identified in advance by monitoring agents, to avoid their failures. The novelty of proposed approach is constant collection and updating of local as well as global information of the system. This policy is determined by calculating weights; taking into account the criticality of the hosts by their monitoring agents which keep updating the weights of hosts. These weights act are used for decision making of checkpointing. These monitoring mobile agents act together to detect undesirable behaviors and also provide support for restoring the system back to normalcy. We also report on the result of reliability and performance issue of our proposed approach. [ABSTRACT FROM PUBLISHER]

Case retrieve is the key link in Case-Based Reasoning(CBR)system, and the distribution of attribute weights affects the retrieval accuracy directly. However, the traditional retrieval methods do not pay much attention to the weights distribution which led to low retrieval efficiency. In this paper, a new method based on Water-Filling principle in wireless communication field is proposed to optimize the case attribute weights. Regarding every single attribute in the case base as a sub channel, then calculates the importance of each case attribute by analyzing the data volatility with the Water-Filling principle. To prove the availability of the method, a glass identification dataset from the UCI database is used for a simulation experiment and the result illustrates that the new method could get a better and more accurate retrieval result compared with the traditional methods. The method could dig the inner information of each case attribute, and could assign proper weight to each attribute, which improves the retrieval accuracy and proves that the method the paper introduced is effective for CBR system. [ABSTRACT FROM PUBLISHER]

This paper studies the application of mathematical expectation-variance in the group decision making. First, the problem of group decision making based on different types of preference information is studied. The mutual transformation relations of preference information are researched. New and more reasonable transformation equations are acquired. Then a method of group decision making is given based on mathematical expectation-variance as an operator. Second, a method is presented for determining the attribute weights based on mathematical expectation-variance. Finally, a numerical example is given to show that the theoretical basis and calculation of the method is easy to be computed and implemented. [ABSTRACT FROM PUBLISHER]

We tackle the problem of determining the beamforming and combining weights in a network of interfering multiple-input multiple-output (MIMO) links. We classify any strategy for computing these weights as either unilateral or bilateral. A unilateral strategy is one for which the responsibility of cancelling interference from one node to another is preassigned to lie solely with only one of the two nodes, so that the other node is free to ignore the interference. Many existing strategies for managing interference in a network of MIMO nodes adopt the unilateral approach. In contrast, a bilateral strategy is one for which the responsibility of cancelling interference from one node to another is not preassigned, but is instead shared by both sides as the weights are computed. We present numerical examples to illustrate that bilateral strategies can significantly outperform unilateral strategies, especially for large networks and high interference. In one example, a bilateral approach delivers an aggregate capacity that is 227% higher than that of the best unilateral approach. We conclude that, although unilateral strategies are useful for determining whether or not the streams allocated in a network of MIMO links can coexist, the weight computation should be done bilaterally to prevent throughput loss. [ABSTRACT FROM PUBLISHER]

Based on analysing least square method, using the least the sum of squares of error criterion, the least the sum of error absolute value criterion and the least the error absolute value criterion, three methods for calculating weights in AHP are proposed. Least square method is a nonlinear programming, but new least squares method is translated into linear system and Minimax method and absolute deviation method are translated into linear programming. The computation method and results are given through a numerical example. The new methods have fast convergence and smaller computational complexity. [ABSTRACT FROM AUTHOR]

Predicted weight magnitudes are not equal when opposite types of influence on a subject agent due to disagreement or agreement by another agent are brought to bear, all else equal. The impact of social influence from others on choices made by subjects and how this influence accumulates was earlier studied by secondary analysis of reported experimental data. A decision threshold model in social influence network theory is used to derive binary decisions as vectors and formulate weights as matrices in order to comprehend the unequal predicted weight magnitudes. [ABSTRACT FROM AUTHOR]

We consider Gaussian quadrature formulæ which exactly integrate a system of tension powers 1,x,x2,...,xn-3, sinh(px), cosh(px), on a given interval [a,b], where n>=4 is an even integer and p>0 is a given tension parameter. In some applications it is essential that p can be changed dynamically, and we need an efficient “on-demand” algorithm that calculates the nodes and weights of Gaussian quadrature formulas for many different values of p, which are not known in advance. It is an interesting numerical challenge to achieve the required full machine precision accuracy in such an algorithm, for all possible values of p. By exploiting various analytic and numerical techniques, we show that this can be done efficiently for all reasonably low values of n that are of any practical importance. [ABSTRACT FROM AUTHOR]

In a previous work, the author introduced inequality constraints into the Hopfield neural networks and clarified how to determine weights in the energy function. The author presently compares his method with the slack neuron method and shows that his method is superior because weights can be determined so that solutions not satisfying inequality constraints become unstable. Taking the n-queen problem as an example, the convergence to the optimal solution is studied. It is shown that for 4- and 5-queen problems, the selection of initial values around the line segment connecting the origin and (1, . . ., 1) almost always gives the optimal solution. For the 6-queen problem, however, the above selection shows poor convergence to the optimal solution. The reason is that for a small-sized problem, the region of initial values where the network converges to the optimal solution includes the neighborhood of the center of the hypercube. But as the size becomes larger, it does not. It is also shown that the convergence of the 6-queen problem is drastically improved by the introduction of a dummy objective function [ABSTRACT FROM PUBLISHER]

The authors study learning in a parallel, neural-network implementation of an image-recognition network recently constructed to synthesize model-based and data-driven approaches to the recognition problem. Learning in this context includes three considerations: (i) learning the basic implications of a hierarchical model-based description, (ii) learning the weights in analogy to conventional neural nets, and (iii) learning new features to update the model. The authors present examples as well as simulation results on new models of learning suggested by optimal control techniques [ABSTRACT FROM PUBLISHER]

The back-propagation algorithm is widely used for learning weights of multilayered neural networks. The major drawbacks however, are the slow convergence and lack of a proper way to set the number of hidden neurons. The author proposes a learning algorithm which solves the above problems. The weights between two layers are successively calculated, fixing other weights so that the error function, which is the square sum of the difference between the training data and the network outputs is minimised. Since the calculation results in solving a set of linearized equations, redundancy of the hidden neurons is judged by the singularity of the corresponding coefficient matrix. For the exclusive-OR and parity check circuits, excellence convergence characteristics are obtained, and the redundancy of the hidden neurons is checked by the singularity of the matrix [ABSTRACT FROM PUBLISHER]

A novel learning method is proposed, which is based on hardware and applicable to analog neural networks. All the network parameters (weights and thresholds) are oscillated slightly with different frequencies, and the spectra detected in the error signal are fed back to the neural network, where the network parameters are changed by a value proportional to the coherent detection signal. Since all parameters are changed in parallel, and the hardware used for learning is composed of analog circuits, this method is suited to high-speed learning. Using exclusive-OR network simulations, the effects of crosstalk between parameter control signals are discussed [ABSTRACT FROM PUBLISHER]