Automatic control of biomass gasifiers using fuzzy inference systems

A fuzzy controller for biomass gasifiers is proposed. Although fuzzy inference systems do not need models to be tuned, a plant model is proposed which has turned out very useful to prove different combinations of membership functions and rules in the proposed fuzzy control. The global control scheme is shown, including the elements to generate the set points for the process variables automatically. There, the type of biomass and its moisture content are the only data which need to be introduced to the controller by a human operator at the beginning of operation to make it work autonomously. The advantages and good performance of the fuzzy controller with the automatic generation of set points, compared to controllers utilising fixed parameters, are demonstrated.     

Hybrid control strategies for a five-finger robotic hand

This paper presents a hybrid controller of soft control techniques, adaptive neuro-fuzzy inference system (ANFIS) and fuzzy logic (FL), and hard control technique, proportional-derivative (PD), for a five-finger robotic hand with 14-degrees-of-freedom (DoF). The ANFIS is used for inverse kinematics of three-link fingers and FL is used for tuning the PD parameters with 2 input layers (error and error rate) using 7 triangular membership functions and 49 fuzzy logic rules. Simulation results with the hybrid of FL-tuned PD controller exhibit superior performance compared to PD, PID and FL controllers alone.     

A Fuzzy Logic Approach to Assess,Manage, and Communicate Carcinogenic Risk

A prospective approach to addressing carcinogen risk assessment is presented. Fuzzy reasoning is used to assess carcinogenic risk, characterize it, and control it. The approach is inspired by fuzzy control inference that deploys linguistic intelligence as input to a system described numerically through membership functions. Fuzzy-based reasoning to estimate carcinogenic risk provides several advantages as discussed here. The fuzzy reasoning approach has more capabilities than traditional models in dealing with risk agents that are probably carcinogens, possibly carcinogens, not classifiable as carcinogens, and probably not carcinogens. Input–output surfaces are presented for each hazard group to enable fast inferencing. Then, a hypothetical example is given to compare the results of traditional methods and the fuzzy-based approach to estimating the risk of a carcinogen to a human population. Results show similarity in risk characterization with less input information to the fuzzy-based approach. Fuzzy reasoning characterizes risk in more explicit and easy to grasp terms. Two outputs of the inferencing system are risk characterization and risk control or remediation.     

The role of human factor in maritime environment risk assessment: A practical application on Ballast Water Treatment (BWT) system in ship

The role of human factor plays a critical role in the safe and clean operation of maritime industry. Human error prediction can be beneficial to assess risk in maritime industry since shipping activities can pose potential hazards to human life and maritime ecology. The aim of this paper is to propose a risk assessment tool by considering the role of human factor. Hence, the desired safety control level in maritime transportation activities can be ascertained. In the proposed approach, a Success Likelihood Index Method (SLIM) extended with fuzzy logic is used to calculate human error probability (HEP). Severity of consequences are adopted in the proposed approach to assess risk. The quantitative risk assessment approach under fuzzy SLIM methodology will be applied to a very specific case on-board ship: Ballast Water Treatment (BWT) system. In order to improve consistency of research and minimize subjectivity of experts' judgments, the paper adopts the dominance factor which is used to adjust the impact level of experts' judgments in the aggregation stage of the methodology. The paper aims at not only highlighting the importance of human factor in maritime risk assessment but also enhancing safety control level and minimizing potential environmental impacts to marine ecology.     

Adaptive mining prediction model for content recommendation to coronary heart disease patients

This paper proposes the Fuzzy Rule-based Adaptive Coronary Heart Disease Prediction Support Model (FbACHD_PSM), which gives content recommendation to coronary heart disease patients. The proposed model uses a mining technique validated by medical experts to provide recommendations. FbACHD_PSM consists of three parts for heart disease risk prediction. First, a fuzzy membership function is constructed using medical guidelines and statistical methods. Then, a decision-tree rule induction technique creates mining-based rules that are subjected to validation by medical experts. As the rules may not be medically suitable, the experts add rules that have been verified and delete inappropriate rules. Thirdly, using fuzzy inference based on Mamdani’s method, the model predicts the risk of heart disease. Based on this, final recommendations are provided to patients regarding normal living, nutrition control, exercise, and drugs. To implement our proposed model and evaluate its performance, we use a dataset from a single tertiary hospital.     

Fuzzy logic for elimination of redundant information of microarray data

Gene subset selection is essential for classification and analysis of microarray data. However, gene selection is known to be a very difficult task since gene expression data not only have high dimensionalities, but also contain redundant information and noises. To cope with these difficulties, this paper introduces a fuzzy logic based pre-processing approach composed of two main steps. First, we use fuzzy inference rules to transform the gene expression levels of a given dataset into fuzzy values. Then we apply a similarity relation to these fuzzy values to define fuzzy equiva- lence groups, each group containing strongly similar genes. Dimension reduction is achieved by considering for each group of similar genes a single representative based on mutual information. To assess the usefulness of this approach, exten- sive experimentations were carried out on three well-known public datasets with a combined classification model using three statistic filters and three classifiers.     

Transitive or Not: A Critical Appraisal of Transitive Inference in Animals

Transitive inference has been historically touted as a hallmark of human cognition. However, the ability of non‐human animals to perform this type of inference is being increasingly investigated. Experimentally, three main methods are commonly used to evaluate transitivity in animals: those that investigate social dominance relationships, the n‐term task series and the less well known associative transitivity task. Here, we revisit the question of what exactly constitutes transitive inference based upon a formal and habitual definition and propose two essential criteria for experimentally testing it in animals. We then apply these criteria to evaluate the existing body of work on this fundamental aspect of cognition using exemplars. Our evaluation reveals that some methods rely heavily on salient assumptions that are both questionable and almost impossible to verify in order to make a claim of transitive inference in animals. For example, we found shortcomings with most n‐term task designs in that they often do not provide an explicit transitive relationship and/or and ordered set on which transitive inference can be performed. Consequently, they rely on supplementary assumptions to make a claim of transitive inference. However, as these assumptions are either impossible or are extremely difficult to validate in non‐human animals, the results obtained using these specific n‐term tasks cannot be taken as unambiguous demonstrations (or the lack thereof) of transitive inference. This realisation is one that is generally overlooked in the literature. In contrast, the associative transitivity task and the dominance relationship test both meet the criteria for transitive inference. However, although the dominance relationship test can disambiguate between transitive inference accounts and associative ones, the associative transitivity test cannot. Our evaluation also highlights the limitations and future challenges of current associative models of transitive inference. We propose three new experimental methods that can be applied within any theoretical framework to ensure that the experimental behaviour observed is indeed the result of transitive inference whilst removing the need for supplementary assumptions: the test for the opposite transitive relation, the discrimination test between two separate and previously non‐reinforced items, and the control for absolute knowledge.     

Fuzzy neural network for the control of high cell density cultivation of recombinant Escherichia coli

A five-layer fuzzy neural network (FNN) was developed for the control of fed-batch cultivation of recombinant Escherichia coli JM103 harboring plasmid pUR 2921. The FNN was believed to represent the membership functions of the fuzzy subsets and to implement fuzzy inference using previous experimental data. This FNN was then used for compensating the exponential feeding rate determined by the feedforward control element. The control system is therefore a feedforward-feedback type. The change in pH of the culture broth and the specific growth rate were used as the inputs to FNN to calculate the glucose feeding rate. A cell density of 84 g DWC/l in the fed-batch cultivation of the recombinant E. coli was obtained with this control strategy. Two different FNNs were then employed before and after induction to enhance plasmid-encoded β-galactosidase production. Before induction the specific growth rate was set as 0.31 h⁻¹, while it was changed to 0.1 h⁻¹ after induction. Compared to when only one FNN was used, the residual glucose concentration could be tightly controlled at an appropriate level by employing two FNNs, resulting in an increase in relative activity of β-galactosidase which was about four times greater. The present investigation demonstrates that a feedforward-feedback control strategy with FNN is a promising control strategy for the control of high cell density cultivation and high expression of a target gene in fed-batch cultivation of a recombinant strain.     

Object-oriented fuzzy expert system for on-line diagnosing and control of bioprocesses

Summary An object-oriented fuzzy expert system to support on-line control of an automated fermentation plant is described. The major elements of the system consist of a fuzzy inference engine, a database, a knowledge base, and an expression evaluater. The expression evaluater calculates specific rates for growth, and substrate and product formation at different physiological states during the cultivation from the measured data. The specific rates are then compared with the standard target rates stored in the database. If differences outside the set tolerances were observed, the inference engine analyses the reasons for the faults on the basis of the knowledge represented in the form of a knowledge network and fuzzy membership functions of the process variables. The fuzzy expert system was developed on the basis of a shell constructed by using the object oriented Smalltalk/V Mac programming environment, with Lactobacillus casei lactic acid fermentation as the example of process application.Visiting scientist from Helsinki University of Technology at RIKEN Correspondence to: P. Linko or I. Endo     

Inference on the strength of balancing selection for epistatically interacting loci

Existing inference methods for estimating the strength of balancing selection in multi-locus genotypes rely on the assumption that there are no epistatic interactions between loci. Complex systems in which balancing selection is prevalent, such as sets of human immune system genes, are known to contain components that interact epistatically. Therefore, current methods may not produce reliable inference on the strength of selection at these loci. In this paper, we address this problem by presenting statistical methods that can account for epistatic interactions in making inference about balancing selection. A theoretical result due to Fearnhead (2006) is used to build a multi-locus Wright-Fisher model of balancing selection, allowing for epistatic interactions among loci. Antagonistic and synergistic types of interactions are examined. The joint posterior distribution of the selection and mutation parameters is sampled by Markov chain Monte Carlo methods, and the plausibility of models is assessed via Bayes factors. As a component of the inference process, an algorithm to generate multi-locus allele frequencies under balancing selection models with epistasis is also presented. Recent evidence on interactions among a set of human immune system genes is introduced as a motivating biological system for the epistatic model, and data on these genes are used to demonstrate the methods.     

Anaerobic granulation technology for wastewater treatment

Anaerobic wastewater treatment using granular sludge reactors is a developing technology, in which granular sludge is the core component. So far, around 900 anaerobic granular sludge units have been operated worldwide. Although intensive research attention has been given to anaerobic granules in the past 20 years, the mechanisms responsible for anaerobic granulation and the strategy of how to expedite substantially the formation of granular sludge have not yet been completely clear. This paper reviews the mode of anaerobic granulation, including the mechanisms and models for anaerobic granulation, major factors influencing anaerobic granulation, characteristics of anaerobic granules, anaerobic granulation in other types of reactors, industrial application of anaerobic granulation technology and neural fuzzy model-based control strategy developed for anaerobic systems. Some approaches for future research are outlined.     

Fuzzy supervisory control of glutamic acid production

In glutamic acid fermentation, the molasses feeding policy and time of penicillin addition significantly affected glutamic acid production, and a fuzzy supervisory control system was developed for their quasi-optimal regulation.From the trend of the experimental data, production rules and membership functions of fuzzy inference were devised to determine the quasi-optimum molasses feeding policy and penicillin addition time. A computer with multitasking operating system was used for the construction of the control system with fuzzy inferencing, which decided the control policy every minute, and the feed rate was controlled automatically. The pattern of residual sugar concentration was almost the same as that of maximum glutamic acid production under manual operation. Using the computer control system, stable production was maintained at the highest level of 71 to 75 g/L. (c) 1994 John Wiley & Sons, Inc.     

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP).     

Intelligence-guided beam angle optimization in treatment planning of intensity-modulated radiation therapy

An intelligence guided approach based on fuzzy inference system (FIS) was proposed to automate beam angle optimization in treatment planning of intensity-modulated radiation therapy (IMRT). The model of FIS is built on inference rules in describing the relationship between dose quality of IMRT plan and irradiated region of anatomical structure. Dose quality of IMRT plan is quantified by the difference between calculated and constraint doses of the anatomical structures in an IMRT plan. Irradiated region of anatomical structure is characterized by the metric, covered region of interest, which is the region of an anatomical structure under radiation field while beam’s eye-view is conform to target volume. Initially, an IMRT plan is created with a single beam. The dose difference is calculated for the input of FIS and the output of FIS is obtained with processing of fuzzy inference. Later, a set of candidate beams is generated for replacing the current beam. This process continues until no candidate beams is found. Then the next beam is added to the IMRT plan and optimized in the same way as the previous beam. The new beam keeps adding to the IMRT plan until the allowed beam number is reached. Two spinal cases were investigated in this study. The preliminary results show that dose quality of IMRT plans achieved by this approach is better than those achieved by the default approach with equally spaced beam setting. It is effective to find the optimal beam combination of IMRT plan with the intelligence-guided approach.     

What's old and new in molecular phylogenetics

While it is fairly easy to devise a phylogenetic tree based on molecular data, it has proven difficult to tell how reliable any such tree is. Thus while the genetic inference that humans, chimpanzees, and gorillas cluster together is widely accepted, the genetic inference that the primary division among Old World human populations is between Asia and EurAfrica is not. A molecular phylogenetic inference linking humans and chimpanzees was proposed in the 1980s based on the technique of DNA hybridization. Despite several recent publications in primary and secondary source material, much confusion still exists surrounding the work. This paper tries to clarify issues that may still be confusing to physical anthropologists, and proposes criteria upon which to judge the robusticity of a phylogenetic inference based on DNA hybridization, in light of a recent published claim of replication. The claim of replication is considered critically. Interestingly, the original DNA hybridization data may actually show a chimp-gorilla link, in harmony with other phylogenetic results.     

Modeling changes in biomass composition during microwave‐based alkali pretreatment of switchgrass

This study used two different approaches to model changes in biomass composition during microwave‐based pretreatment of switchgrass: kinetic modeling using a time‐dependent rate coefficient, and a Mamdani‐type fuzzy inference system. In both modeling approaches, the dielectric loss tangent of the alkali reagent and pretreatment time were used as predictors for changes in amounts of lignin, cellulose, and xylan during the pretreatment. Training and testing data sets for development and validation of the models were obtained from pretreatment experiments conducted using 1–3% w/v NaOH (sodium hydroxide) and pretreatment times ranging from 5 to 20 min. The kinetic modeling approach for lignin and xylan gave comparable results for training and testing data sets, and the differences between the predictions and experimental values were within 2%. The kinetic modeling approach for cellulose was not as effective, and the differences were within 5–7%. The time‐dependent rate coefficients of the kinetic models estimated from experimental data were consistent with the heterogeneity of individual biomass components. The Mamdani‐type fuzzy inference was shown to be an effective approach to model the pretreatment process and yielded predictions with less than 2% deviation from the experimental values for lignin and with less than 3% deviation from the experimental values for cellulose and xylan. The entropies of the fuzzy outputs from the Mamdani‐type fuzzy inference system were calculated to quantify the uncertainty associated with the predictions. Results indicate that there is no significant difference between the entropies associated with the predictions for lignin, cellulose, and xylan. It is anticipated that these models could be used in process simulations of bioethanol production from lignocellulosic materials. Biotechnol. Bioeng. 2010;105: 88–97. © 2009 Wiley Periodicals, Inc.     

Ozone prediction on the basis of neural networks,support vector regression and methods with uncertainty

The article presents modeling of daily average ozone level prediction by means of neural networks, support vector regression and methods based on uncertainty. Based on data measured by a monitoring station of the Pardubice micro-region, the Czech Republic, and optimization of the number of parameters by a defined objective function and genetic algorithm a model of daily average ozone level prediction in a certain time has been designed. The designed model has been optimized in light of its input parameters. The goal of prediction by various methods was to compare the results of prediction with the aim of various recommendations to micro-regional public administration management. It is modeling by means of feed-forward perceptron type neural networks, time delay neural networks, radial basis function neural networks, ε-support vector regression, fuzzy inference systems and Takagi–Sugeno intuitionistic fuzzy inference systems. Special attention is paid to the adaptation of the Takagi–Sugeno intuitionistic fuzzy inference system and adaptation of fuzzy logic-based systems using evolutionary algorithms. Based on data obtained, the daily average ozone level prediction in a certain time is characterized by a root mean squared error. The best possible results were obtained by means of an ε-support vector regression with polynomial kernel functions and Takagi–Sugeno intuitionistic fuzzy inference systems with adaptation by means of a Kalman filter.     

Fuzzy logic for phytosociology

The present paper aims to give an analysis of properties of the phytosociological language, which relates to the vagueness of some concepts in vegetation science.A translation of the simplest synsystematic propositions into possibility distributions has been proposed. Inferential relationships between sentences mentioned using the concept of semantic entailment have been reported.Sentences describing habitat requirements of syntaxa are in fact disguised conditionals; their paraphrases have been given the form of implication. Since these sentences include fuzzy predicates their meaning is a fuzzy relation. The latter may constitute the basis of prediction by means of the compositional rule of inference.     

Control model of human stance using fuzzy logic

 A control model of human stance is proposed based on knowledge from behavioral experiments and physiological systems. The proposed model is based on the control of global variables specific to body orientation and alignment, rather than on the control of the body’s center of mass within the base of support. Furthermore, the proposed control model is not based on purely inverted pendulum body mechanics where only motion at one joint is controlled, as for instance the ankle. In the proposed model, the degrees of freedom are controlled by using reciprocal and synergistic muscle actions at multiple joints. The control model is based on three sets of different global variables which act in parallel: (1) limb length and its derivative, (2) limb orientation and its derivative, and (3) trunk attitude and its derivative. An important feature of the control model is the use of fuzzy logic, which enables us to model experimental findings and physiological knowledge in a meaningful and explicit way using fuzzy if-then rules. In the control model, 36 fuzzy if-then rules are implemented and applied using a four-linked segment model consisting of a trunk, thigh, shank and foot. Uni- and biarticular limb muscles and trunk muscles are represented as torque actuators at each individual joint. In the model, three sets of global variables act in parallel and make corrective and coordinated responses to internal, self-induced perturbations. The data show that the use of global variables and fuzzy logic successfully enables us to model human standing with sway about a point of equilibrium. Small changes in, for example, total body sway are comparable to those seen during natural sway in human stance. The selected controllers—limb length, limb orientation and trunk attitude—seem to be appropriate for human stance control. Received: 30 October 1996/Accepted in revised form: 7 April 1997     

FUZZY SET THEORY APPLIED TO PRODUCT CLASSIFICATION BY A SENSORY PANEL1

It is frequently impossible to meet the assumptions underlying the statistical approach to classification of food products by a sensory panel. To find an alternative, we have investigated the applicability of the fuzzy set theory. Within a fuzzy set framework it is acceptable that a product belongs to several classes simultaneously and no assumptions regarding the distribution of sensory properties for a product class are made. Fuzzy classification models can be constructed from a set of training objects by linking the soft class labels to the sensory attributes applying an inference procedure based on fuzzy logic. A number of fuzzy inference procedures has been evaluated using a number of attribute sets. A satisfactory classification has been found using a very simple implication rule and a set of three attributes.