Michifumi Yoshioka

Self-tuning neuro-PID control and applications

In this paper, we propose a method to use the neural networks to tune the PID (proportional plus integral plus derivative) gains such that human operators tune the gains adaptively according to the environmental condition and systems specification. The tuning method is based on the error backpropagation method and hence it may be trapped in a local minimum. In order to avoid the local minimum problem, we use the genetic algorithm to find the initial values of the connection weights of the neural network and initial values of PID gains. The experimental results show the effectiveness of the present approach.

Neuro-PID control for inverted single and double pendulums

The paper is concerned with a new architecture of a self-tuning neuro-PID control system and its application to stabilization of an inverted pendulum. A single-input multi-output system is considered to control the inverted pendulum by using the PID controller. The PID gains are tuned by using two kinds of neural network. The simulation results show effectiveness of the proposed approach.

A multi-objective strategy in genetic algorithms for gene selection of gene expression data

A microarray machine offers the capacity to measure the expression levels of thousands of genes simultaneously. It is used to collect information from tissue and cell samples regarding gene expression differences that could be useful for cancer classification. However, the urgent problems in the use of gene expression data are the availability of a huge number of genes relative to the small number of available samples, and the fact that many of the genes are not relevant to the classification. It has been shown that selecting a small subset of genes can lead to improved accuracy in the classification. Hence, this paper proposes a solution to the problems by using a multiobjective strategy in a genetic algorithm. This approach was tried on two benchmark gene expression data sets. It obtained encouraging results on those data sets as compared with an approach that used a single-objective strategy in a genetic algorithm.

Sentiment Analysis of Stock Market News with Semi-supervised Learning

In these days, there are many news on stock market on the Internet and investors have to understand them immediately to invest in a stock market. In this study we determine sentimental polarities of the stock market news using a polarity dictionary, which consists of terms and their polarities. To achieve our aim we have to construct the polarity dictionary automatically because of decrease of human efforts. In construction the dictionary we use a semi-supervised learning approach. In the semi-supervised approach at first we make a small polarity dictionary, which a word polarity is determined manually, and using many stock market news, which polarities are not known, new words are added in the polarity dictionary. In this paper we proposed an automatically dictionary construction approach and sentiment analysis of stock market news using the dictionary. To discuss our proposed method we compare polarities determined by a financial expert with polarities determined with our proposed method. Hence, we confirm that the proposed method can make an appropriate dictionary.

Life cycle of CO2-emissions from electric vehicles and gasoline vehicles utilizing a process-relational model

This article aims at estimating life cycle CO2 emissions from electric vehicles (EV) and gasoline vehicles (GV), although the estimation in this study is not an LCA according to ISO14040s. For this purpose, a mathematical tool called the Process-relational model was developed. The Process-relational model is used for establishing life cycle inventories. The model has a structure which improved the principle of input-output analysis in econometrics that only one product is generated by one process. This model enabled us to overcome difficulties of LCA in retracing complicated repercussions among production systems.Then, life cycle CO2, emissions from electric vehicles (EV) and gasoline vehicles (GV) were estimated with this model. Estimated results indicated that the manufacture and driving of EV resulted in less CO2 emissions than chose of GV. However, the difference between EV and GV dramatically changed depending on traffic situations. Namely, the difference became larger as the average velocity of the vehicles became lower. We also compared CO2, emission from manufacturing EV with that from driving EV. The share of manufacture was shown to increase in total CO2, emissions as the average velocity of the EV became higher. In conclusion, we clarified the direction of research and development of EV and GV for reducing the life cycle CO2.

Intelligent Electronic Nose Systems for Fire Detection Systems Based on Neural Networks

In this paper, an intelligent electronic nose (EN)system designed using cheap metal oxide gas sensors (MOGS) is designed to detect fires at an early stage. The time series signals obtained from the same source of fire are highly correlated, and different sources of fire exhibit unique patterns in the time series data. Therefore, the error back propagation (BP) method can be effectively used for the classification of the tested smell. The accuracy of 99.6% is achieved by using only a single training dataset from each source of fire. The accuracy achieved with the k-means algorithm is 98.3%, which also shows the high ability of the EN in detecting the early stage of fire from various sources.

An enhancement of binary particle swarm optimization for gene selection in classifying cancer classes

BackgroundGene expression data could likely be a momentous help in the progress of proficient cancer diagnoses and classification platforms. Lately, many researchers analyze gene expression data using diverse computational intelligence methods, for selecting a small subset of informative genes from the data for cancer classification. Many computational methods face difficulties in selecting small subsets due to the small number of samples compared to the huge number of genes (high-dimension), irrelevant genes, and noisy genes.MethodsWe propose an enhanced binary particle swarm optimization to perform the selection of small subsets of informative genes which is significant for cancer classification. Particle speed, rule, and modified sigmoid function are introduced in this proposed method to increase the probability of the bits in a particle’s position to be zero. The method was empirically applied to a suite of ten well-known benchmark gene expression data sets.ResultsThe performance of the proposed method proved to be superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also requires lower computational time compared to BPSO.

Stabilization of double inverted pendulum with self-tuning neuro-PID

A self-tuning neuro-PID control architecture is proposed and applied to the stabilization of double inverted pendulum. The gain parameters of the PID controller are tuned using a neural network. The effectiveness of the proposed method is shown through simulation and experiment.

Particle swarm optimization for gene selection in classifying cancer classes

The application of microarray data for cancer classification has recently gained in popularity. The main problem that needs to be addressed is the selection of a small subset of genes from the thousands of genes in the data that contribute to a disease. This selection process is difficult due to the availability of a small number of samples compared with the huge number of genes, many irrelevant genes, and noisy genes. Therefore, this article proposes an improved binary particle swarm optimization to select a near-optimal (small) subset of informative genes that is relevant for the cancer classification. Experimental results show that the performance of the proposed method is superior to the standard version of particle swarm optimization (PSO) and other previous related work in terms of classification accuracy and the number of selected genes.

Selecting informative genes from microarray data by using hybrid methods for cancer classification

Gene expression technology, namely microarrays, offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. Microarray data are expected to be of significant help in the development of an efficient cancer diagnosis and classification platform. A major problem in these data is that the number of genes greatly exceeds the number of tissue samples. These data also have noisy genes. It has been shown in literature reviews that selecting a small subset of informative genes can lead to improved classification accuracy. Therefore, this paper aims to select a small subset of informative genes that are most relevant for cancer classification. To achieve this aim, an approach using two hybrid methods has been proposed. This approach is assessed and evaluated on two well-known microarray data sets, showing competitive results.