東一郎 中川

Bio: 東一郎 中川 is an academic researcher. The author has an hindex of 1, co-authored 1 publications receiving 33 citations.

Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach

Abstract: This work describes a forward-looking approach for the solution of dynamic (time-changing) problems using evolutionary algorithms. The main idea of the proposed method is to combine a forecasting technique with an evolutionary algorithm. The location, in variable space, of the optimal solution (or of the Pareto optimal set in multi-objective problems) is estimated using a forecasting method. Then, using this forecast, an anticipatory group of individuals is placed on and near the estimated location of the next optimum. This prediction set is used to seed the population when a change in the objective landscape arrives, aiming at a faster convergence to the new global optimum. The forecasting model is created using the sequence of prior optimum locations, from which an estimate for the next location is extrapolated. Conceptually this approach encompasses advantages of memory methods by making use of information available from previous time steps. Combined with a convergence/diversity balance mechanism it creates a robust algorithm for dynamic optimization. This strategy can be applied to single objective and multi-objective problems, however in this work it is tested on multi-objective problems. Initial results indicate that the approach improves algorithm performance, especially in problems where the frequency of objective change is high.

Estimation of Directional Wave Spectra from Measured Ship Responses

Abstract: This thesis focuses on methodologies for estimation of directional wave spectra. The objective is to study and develop methods for estimating wave spectra on the basis of measured ship responses. Traditionally, such methods, which are derived by use of linear spectral analysis, are based on either a parametric or a non-parametric procedure. In the parametric procedure the estimated wave spectrum is assumed to be composed of one parameterised spectrum or by the summation of several parameterised spectra, e.g. the generalised JONSWAP spectrum, so that it is the underlying wave parameters which are sought for. The non-parametric method, however, yields the directional wave spectrum in a number of discretised points of the wave field. In the thesis both procedures are dealt with, but with the very focus on the non-parametric method. The non-parametric method, called the Bayesian estimation method, is based on Bayesian modelling, which introduces the use of prior information to make the directional wave spectrum smooth and to avoid an unrealistic solution. The topic of on-site estimation of directional wave spectra from measured ship responses has its overall relevance in connection with decision support systems for safe navigation of ships. For this reason it is important that all the procedures used for estimation are fully automated. Thus, a cross spectral analysis of the measured responses is derived, so that the governing equations can be found automatically. Hence, so-called multivariate autoregressive (MAR) procedures are treated in detail. Moreover, general aspects in relation to decision support systems are treated in the thesis. In order to verify the Bayesian estimation method, numerical simulations are carried out. Hence, from generated ship responses based on a theoretically known wave spectrum, the Bayesian estimation method is shown to predict the wave spectrum with good accuracy. Following the numerical simulations, two different sets of full-scale data are analysed. In the first set, motion measurements from four test trials with a research and training ship form the basis of the estimated wave spectra. The wave parameters of the estimated spectra are compared with parameters from visual observations and, in general, the agreement is reasonable. The other set of full-scale data stems from a container ship and, in addition iii iv to the motion measurements, knowledge of the on-site wave spectra from a wave radar system is at hand. These wave spectra are compared with the results from the Bayesian method and the underlying wave parameters are found with fair consistency between the two estimations. However, some discrepancies are observed for the data characterised by the severest sea states. Due to lack of redundancy in the data, it is not possible to settle which estimation is the most correct. In general, the developed method is capable of estimating directional wave spectra on the basis of measured ship responses, and at the very end of the thesis, suggestions for improving the specific method are given.

An Akaike State-Space Controller for RBF-ARX Models

Abstract: Radial basis function autoregressive with exogenous inputs (RBF-ARX) models have been shown to be useful in modeling the nonlinear behavior of a variety of complex systems. In particular, Peng have shown how the RBF-ARX model may be used to model the selective catalytic reduction (SCR) process for real data from a thermal power plant, and have simulated control of the plant using the generalized predictive control (GPC) method of Clarke very effectively. However, the GPC approach requires constrained nonlinear optimization at each control step, which is time-consuming and computationally very expensive. Here, in place of the GPC approach, the authors use a variation of the Kalman state-space approach to control, which involves only the solution of a set of Riccati equations at each step. As is well known, the usual Kalman state-space representation breaks down when we need to control a system depending on inputs extending several lags into the past, but to avoid this problem, we have used the state-space approach of Akaike and Nakagawa. Although this was originally developed for the linear case, here we show how the representation may be extended for use with the nonlinear RBF-ARX model. The straightforward tuning procedure is illustrated by several examples. Comparisons with the GPC method also show the effectiveness and computational efficiency of the Akaike state-space controller method. The robustness of the method is demonstrated by showing how the RBF-ARX model fitted to one data sequence from the SCR process may be used to construct a high performance controller for other sequences taken from the same process. Akaike state-space control may also be easily extended to the multi-input-multi-output case, making it widely applicable in practice.

A data-driven model of the generation of human EEG based on a spatially distributed stochastic wave equation

Abstract: We discuss a model for the dynamics of the primary current density vector field within the grey matter of human brain. The model is based on a linear damped wave equation, driven by a stochastic term. By employing a realistically shaped average brain model and an estimate of the matrix which maps the primary currents distributed over grey matter to the electric potentials at the surface of the head, the model can be put into relation with recordings of the electroencephalogram (EEG). Through this step it becomes possible to employ EEG recordings for the purpose of estimating the primary current density vector field, i.e. finding a solution of the inverse problem of EEG generation. As a technique for inferring the unobserved high-dimensional primary current density field from EEG data of much lower dimension, a linear state space modelling approach is suggested, based on a generalisation of Kalman filtering, in combination with maximum-likelihood parameter estimation. The resulting algorithm for estimating dynamical solutions of the EEG inverse problem is applied to the task of localising the source of an epileptic spike from a clinical EEG data set; for comparison, we apply to the same task also a non-dynamical standard algorithm.