MISC - TOMIDA Shuta

Analysis of expression profile using fuzzy adaptive resonance theory

S Tomida, T Hanai, H Honda, T Kobayashi

BIOINFORMATICS   18 ( 8 )   1073 - 1083   2002.8

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Language：English   Publisher：OXFORD UNIV PRESS  

Motivation: It is well understood that the successful clustering of expression profiles give beneficial ideas to understand the functions of uncharacterized genes. In order to realize such a successful clustering, we investigate a clustering method based on adaptive resonance theory (ART) in this report.
Results: We apply Fuzzy ART as a clustering method for analyzing the time series expression data during sporulation of Saccharomyces cerevisiae. The clustering result by Fuzzy ART was compared with those by other clustering methods such as hierarchical clustering, k-means algorithm and self-organizing maps (SOMs). In terms of the mathematical validations, Fuzzy ART achieved the most reasonable clustering. We also verified the robustness of Fuzzy ART using noised data. Furthermore, we defined the correctness ratio of clustering, which is based on genes whose temporal expressions are characterized biologically. Using this definition, it was proved that the clustering ability of Fuzzy ART was superior to other clustering methods such as hierarchical clustering, k-means algorithm and SOMs. Finally, we validate the clustering results by Fuzzy ART in terms of biological functions and evidence.

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Construction of COD simulation model for activated sludge process by recursive fuzzy neural network

S Tomida, T Hanai, H Honda, T Kobayashi

JOURNAL OF CHEMICAL ENGINEERING OF JAPAN   34 ( 3 )   369 - 375   2001.3

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Language：English   Publisher：SOC CHEMICAL ENG JAPAN  

Using a fuzzy neural network (FNN), we constructed a simulation model which estimates the effluent chemical oxygen demand (COD) value from daily routine measurements. Since the water quality of wastewater is changing day by day, an FNN model with a recursively renewing method of learning data (R-FNN) is proposed. With this R-FNN, Learning data used to construct an FNN model are renewed with elapsed time so as to estimate the effluent COD value with good accuracy. The estimation results for 9 weeks data using R-FNN were compared with those using a conventional FNN. The average error using the R-FNN model was 0.36 mg/l, while that using the conventional FNN was 1.50 mg/l. Moreover, estimation of the effluent COD throughout one year was carried out, and the average error was only 0.40 mg/l. This result can show the usefulness of the R-FNN for the simulation model of the activated sludge process.

DOI： 10.1252/jcej.34.369

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Construction of COD simulation model for activated sludge process by fuzzy neural network

S Tomida, T Hanai, N Ueda, H Honda, T Kobayashi

JOURNAL OF BIOSCIENCE AND BIOENGINEERING   88 ( 2 )   215 - 220   1999.8

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Language：English   Publisher：SOC BIOSCIENCE BIOENGINEERING JAPAN  

Fuzzy neural network (FNN) was applied to construct a simulation model for estimating the effluent chemical oxygen demand (COD) value of an activated sludge process in a "U" plant, in which most of process variables were measured once an hour. The constructed FNN model could simulate periodic changes in COD with high accuracy. Comparing the simulation result obtained using the FNN model with that obtained using the multiple regression analysis (MRA) model, it was found that the FNN model had 3.7 times higher accuracy than the MRA model. The FNN models corresponding to each of the four seasons were also constructed. Analyzing the fuzzy rules acquired from the FNN models after learning, the operational characteristic of this plant could be elucidated. Construction of the simulation model for another plant "A", in which process variables were measured once a day, was also carried out. This FNN model also had a relatively high accuracy.

DOI： 10.1016/S1389-1723(99)80205-9

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