In an artificial Spiking Neural Network (SNN) the information processing and transmission are carried out by spike trains in a manner similar to the generic biological neurons. Recently it has been reported that they are computationally more powerful than the conventional neural networks. Yet, there are no well defined efficient methods for learning due to their rather intricately discontinuous and nonlinear mechanisms. In this paper, we consider a recurrent SNN constructed with integrate-and-fire type spiking neurons. First we propose a learning method such that the SNN possesses desired transient responses (spike-train outputs) by changing the synaptic weights. Further by including periodic state conditions we propose a learning method such that the SNN possesses desired oscillatory responses (limit cycle spike train) by changing both the synaptic weights and the initial conditions. Simulation examples are also provided to verify the efficiency and the applicability of the proposed algorithm.
This paper studies the control model of hot metal temperature (HMT) in the blast furnace, from the view point of control theory, and clarifies that (1) HMT can be stabilized using the feedback of the HMT at the lower area of blast furnace, when it can be measured, (2) the estimation model of HMT at the lower area of blast furnace should be developed and be included in the control system, when it can't be measured, and (3) the model should estimate not only the dynamics of HMT change against actions like blast temperature change but also that of the HMT change caused by disturbance factor. Furthermore, the clarified properties are applied to blow-in operation planning and contribute to the short term planning.
This paper discusses a motion planning method for mobile robots in large scale transportation systems such as transportation of parts in factory, wafers in semiconductor factory, inspection objects in hospital and so on. Recently, mobile robots in such systems have become complex with an increasing number of transships for transportation. The problem is to determine the appropriate traversal route for each mobile robots improving efficiency of transportation. First, this problem is divided into several zones in which Genetic algorithms are applied to search an optimal traversal route for each zones. Then, the results in all zones are combined to determine a total traversal route by Genetic algorithms. The proposed method enabled us to solve large scale transportation problem in practicable computing time for online use.
In this paper, we propose an optical measurement system for a nonholonomic wheeled robot, and develop a state estimation method and a measurement-output feedback controller. Since the kinematic model of such a wheeled mobile robot is expressed as a driftless nonlinear state equation, there arises a difficulty in observer design that the observability depends on control inputs. This matter is settled by transforming the time-scale of the state equation to a new one, which represents the trajectory of the mobile robot. Then we construct a nonlinear observer which achieves local exponential estimation-error convergence and allows us to specify its rate, where the design method is based on “the extended Luenberger observer (Zeitz)”. Finally, we apply a control strategy for a class of driftless systems based on “time-state control form (Sampei et al.)” to the position control problem by combining it with the observer to form an output feedback controller. Some results of numerical simulations are also presented.
Lyapunov equations and inequalities are studied in connection with the stability of linear periodic systems. First, as preliminaries, existing results on Lyapunov equations in time-invariant systems are summarized in the form of equivalence theorems. Then, based on those results, existence conditions are derived for positive-definite or nonnegative-definite periodic solutions of periodic Lyapunov differential equations and inequalities with reference to the detectability and observability. Such conditions are stated all as equivalent conditions for the system to be either asymptotically stable or stable. Brief discussions are included also for periodic Lyapunov difference equations in discrete-time periodic systems.
In this paper, we construct a genetic algorithm (GA) for location problems of urban facilities. In the encoding of the GA, loci and alleles are defined as sites for placements and types of the facilities, respectively. An individual is a planar array. The genetic operators are selection, crossover and mutation. In the selection, roulette selection and elitist preserving selection are used. In the crossover, 2 selected individuals are each divided into 4 by 2 straight lines which are selected at random. One of the 4 divided parts is selected at random. The selected part is changed between the 2 individuals. In the mutation, a facility or a residence is randomly placed in the randomly selected locus. For fitness, the GA uses the results of the evaluating system which we have proposed. We execute simulation for placement of urban facilities and consider the results of the simulation.