In this paper, we propose an ANP framework for group decision-making problem with incomplete information. The proposed method consists of three parts, namely, aggregation of weights of alternatives, aggregation of weights of criteria and evaluation of global weights. In aggregating weights of alternatives, we calculate group weights from individual weights, which have been calculated by some AHP, by using a quadratic programming problem. In aggregating the weights of criteria, we calculate the weights for evaluating an alternative from the matrix, which is geometric average of pairwise comparison matrices of decision makers only who evaluate the alternative. Then the weights of criteria may be different in each alternative. Hence, we use the supermatrix method of ANP for evaluating global weights. A small numerical example shows that our proposed framework enables us to obtain the global weights of a group decision-making problem with incomplete information.
Market-oriented programming is a multi-agent-based concept to facilitate distributed problem solving. In market-oriented programming we take the metaphor of an economy computing multiagent behaviour literally, and directly implement the distributed computation as a market price system. In a market-oriented programming approach to distributed problem solving, the resource allocation for a set of computational agents is derived by computing competitive market of an artificial economy. Since heterogeneous agent strategies have intensive effects on the resource allocation, dynamism, we analyse and systematise their relationship in supply chain simulation with a virtual market.The behaviour of the system can be analysed in agent interactions based on economic strategies.
The Jordan canonical form of a matrix plays an important role in control system theory. But the numerically stable computation method has not been established. In this paper, we propose a numerical algorithm for the computation of Jordan canonical form of matrix. The transformation matrix consists of principal vectors obtained by the intersection of the kernel of the eigen-vector spaces. And we define the numerical Jordan canonical form of matrix and propose a more stable numerical method. The numerical example shows that the proposed method is more reliable than the well-known commercially available software.
Reactive scheduling approach is considered effective in a manufacturing system, which undergoes various types of delays due to disruption such as machine breakdowns, urgent jobs and so on. This is because it aims to reduce delays of the schedule by revising it partially for some jobs. This paper deals with the problem in what situation we should conduct schedule revision in reactive scheduling in order to obtain an efficient revised schedule. In this paper, we focus on the number of delayed tasks based on the concept ofmatch-upto demonstrate the existence of a suitable number of delayed tasks by computational simulation which can be a criterion to determine the schedule revision timing. An optimal policy to determine the timing of schedule revision is also suggested.
In this paper, we solve the problem of global output regulation for a single-input single-output nonlinear system driven by a globally stable exosystem. The proposed approach does not require the system to have a relative degree. Some invariant manifolds that specify the behavior of the system are introduced to transform the system repeatedly. In order to guarantee the global stability of the system on these manifolds, a backstepping-like design method is adopted. The global output regulation of the system is shown to be performed by a state feedback law under linear growth conditions and several assumptions. We demonstrate the effectiveness of proposed method through a numerical simulation.
The robust stability problem for a class of uncertain linear systems with time-varying delays in the state is discussed. The uncertainty is assumed to be norm-bounded and appears in all matrices of the state-space model. A robust stability condition being dependent on the length of the delay is derived in the form of linear matrix inequalities (LMIs), which is less conservative than several previous delay independent or delay dependent conditions.
A behavior network and its learning rule are proposed as an autonomous motion acquisition method for an agent in an unknown environment. The proposed method is applied to a learning of the collision avoidance by a simulation model of Khepera. The motion is decomposed into 8 behavior elements, and the proposed behavior network is a simple one-layered network, which maps a vector in an input sensor space to one output unit corresponding to each behavior element. The weights are obtained by Hebbian type learning according to a given reward, which evaluates the behavior chosen by the network. The structure of the network, which is decomposable into the behavior elements, enables a straightforward expansion of the network by the addition of output units. A simulation shows the successful learning of the agent, and more various behavior patterns are obtained by the expansion of the network. The proposed method is easily applied to other motion acquisition tasks owing to the simplicity of the network structure and the learning rule, and an example is demonstrated by a learning simulation of the reaching task by the same model.
In this paper, an identification problem of uncertain parameters which are involved in the mathematical model is investigated for a class of stochastic uncertain systems. First, it is shown that the parameter identification can be achieved by solving the state estimation and the least-squares problem simultaneously. Secondly, a modified Gauss-Newton method is proposed, showing that it has contraction property. Finally, a numerical example is shown to demonstrate the efficiancy of the identification method proposed here for a process modeled by a first-order system with time-delay.
Achievable performance of preview and delayed control is investigated in terms of H∞ criterion. We defined a generalized class of H∞ problem, in which both control and disturbance involve multiple delays, and provide a check method of solvability based on a solution to matrix Riccati equation. The posed problem covers robust H∞ preview control problem, the H∞ control for multiple input delay systems, and further it enables to derive formulas of LQ control for multiple input delay systems.