Knowledge extraction from evolving spiking neural networks with rank order population coding

This paper demonstrates how knowledge can be extracted from evolving spiking neural networks with rank order population coding. Knowledge discovery is a very important feature of intelligent systems. Yet, a disproportionally small amount of research is centered on the issue of knowledge extraction from spiking neural networks which are considered to be the third generation of artificial neural networks. The lack of knowledge representation compatibility is becoming a major detriment to end users of these networks. We show that a high-level knowledge can be obtained from evolving spiking neural networks. More specifically, we propose a method for fuzzy rule extraction from an evolving spiking network with rank order population coding. The proposed method was used for knowledge discovery on two benchmark taste recognition problems where the knowledge learnt by an evolving spiking neural network was extracted in the form of zero-order Takagi-Sugeno fuzzy IF-THEN rules.     

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.     

Growing of a Fuzzy Recurrent Artificial Neural Network (FRANN) for pattern classification

This paper describes a method for growing a recurrent neural network of fuzzy threshold units for the classification of feature vectors. Fuzzy networks seem natural for performing classification, since classification is concerned with set membership and objects generally belonging to sets of various degrees. A fuzzy unit in the architecture proposed here determines the degree to which the input vector lies in the fuzzy set associated with the fuzzy unit. This is in contrast to perceptrons that determine the correlation between input vector and a weighting vector. The resulting membership value, in the case of the fuzzy unit, is compared with a threshold, which is interpreted as a membership value. Training of a fuzzy unit is based on an algorithm for linear inequalities similar to Ho-Kashyap recording. These fuzzy threshold units are fully connected in a recurrent network. The network grows as it is trained. The advantages of the network and its training method are: (1) Allowing the network to grow to the required size which is generally much smaller than the size of the network which would be obtained otherwise, implying better generalization, smaller storage requirements and fewer calculations during classification; (2) The training time is extremely short; (3) Recurrent networks such as this one are generally readily implemented in hardware; (4) Classification accuracy obtained on several standard data sets is better than that obtained by the majority of other standard methods; and (5) The use of fuzzy logic is very intuitive since class membership is generally fuzzy.     

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.     

APPLICATION OF FUZZY SETS AND NEURAL NETWORKS IN SENSORY ANALYSIS

This paper describes an initial but fundamental attempt to lay some groundwork for a fuzzy-set-based paradigm for sensory analysis and to demonstrate how fuzzy set and neural network techniques may lead to a natural way for sensory data interpretation. Sensory scales are described as fuzzy sets, sensory attributes as fuzzy variables, and sensory responses as sample membership grades. Multi-judge responses are formulated as a fuzzy membership vector or fuzzy histogram of response, which gives an overall panel response free of the unverifiable assumptions implied in conventional approaches. Neural networks are used to provide an effective tool for modeling and analysis of sensory responses in their naturally fuzzy and complex forms. A maximum method of defuzzification is proposed to give a crisp grade of the majority opinion. Two applications in meat quality evaluation are used to demonstrate the use of the paradigm and procedure. It is hoped that this work will bring up some new ideas and generate interest in research on application of fuzzy sets and neural networks in sensory analysis.     

Inclusion of fractal dimension in four machine learning algorithms improves the prediction accuracy of mean weight diameter of soil

The study of soil mean weight diameter (MWD), essential for sustainable soil management, has recently received much attention. As the estimation of MWD is challenging, labor-intensive, and time-consuming, there is a crucial need to develop a predictive estimation method to generate helpful information required for the soil health assessment to save time and cost involved in soil analysis. Pedotransfer functions (PTFs) are used to estimate parameters that are ‘difficult to measure’ and time-consuming with the help of ’easy to measure’ parameters. In the current study, empirical PTFs, i.e., multi-linear regression (MLR), and four machine learning based PTFs, i.e., artificial neural network (ANN), support vector machine (SVM), classification and regression trees (CART), and random forest (RF) were used for mean weight diameter prediction in Karnal district of Haryana, India. A total of 121 soil samples from 0‐15 and 15‐30 cm soil depths were collected from seventeen villages of Nilokheri, Nissing, and Assandh blocks of Karnal district. Soil parameters such as bulk density (BD), fractal dimension (D), soil texture (i.e., sand, silt, and clay), organic carbon (OC), and glomalin content were used as the input variables. Two input combinations, i.e., one with texture data (dataset 1) and the other with fractal dimension data replacing texture (dataset 2), were used, and the complete dataset (121) was divided into training and testing datasets in a 4:1 ratio. The model performance was evaluated by statistical parameters such as mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), normalized root mean square error (NRMSE), and determination coefficient (R²). The comparison results showed that including the fractal dimension in the input dataset improved the prediction capability of ANN, SVM, and RF. MLR and CART showed lower predictive ability than the other three approaches (i.e., ANN, SVM, and RF). In the training dataset, RMSE (mm) for the SVM model was 8.33% lower with D than with texture as the input, whereas, in the testing dataset, it was 16.67% lower. Because SVM is more flexible and effectively captures non-linear relationships, it performed better than the other models in predicting MWD. As seen in this study, the SVM model with input data D is the best in its class and has a high potential for MWD prediction in the Karnal district of Haryana, India.     

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.     

Adaptive neuro-fuzzy inference system: Estimation of soil aggregates stability

In present study, the capabilities of multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) in developing pedotransfer functions (PTFs) for estimating geometric mean diameter (GMD) and mean weight diameter (MWD), from routine soil properties and combination of routine soil properties and fractal dimension of aggregates were evaluated. For this reason 101 samples were collected form the Northwest of Iran and some their properties such as soil texture, pH, cation exchange capacity (CEC), and organic matter (OM), fractal dimension of aggregates between number-diameter (Dn), mass-diameter (Dmt), and bulk density-diameter (Dmy) were determined and used as an input variables for determining of mean weight diameter (MWD) and geometric mean diameter (GMD) by MLR and ANFIS PTFs. Results showed that the application of fractal dimension of aggregates as a predictor in two methods improved the accuracy of PTFs. As well as, results showed that ANFIS have greater potential for determination of the relationships between soil aggregate stability indices and other soil properties in compared with MLR. Therefore using of adaptive neuro-fuzzy inference system (ANFIS) in developing pedotransfer functions is recommended.     

Likelihood-based inferences under isolation by distance: two-dimensional habitats and confidence intervals

Likelihood-based methods of inference of population parameters from genetic data in structured populations have been implemented but still little tested in large networks of populations. In this work, a previous software implementation of inference in linear habitats is extended to two-dimensional habitats, and the coverage properties of confidence intervals are analyzed in both cases. Both standard likelihood and an efficient approximation are considered. The effects of misspecification of mutation model and dispersal distribution, and of spatial binning of samples, are considered. In the absence of model misspecification, the estimators have low bias, low mean square error, and the coverage properties of confidence intervals are consistent with theoretical expectations. Inferences of dispersal parameters and of the mutation rate are sensitive to misspecification or to approximations inherent to the coalescent algorithms used. In particular, coalescent approximations are not appropriate to infer the shape of the dispersal distribution. However, inferences of the neighborhood parameter (or of the product of population density and mean square dispersal rate) are generally robust with respect to complicating factors, such as misspecification of the mutation process and of the shape of the dispersal distribution, and with respect to spatial binning of samples. Likelihood inferences appear feasible in moderately sized networks of populations (up to 400 populations in this work), and they are more efficient than previous moment-based spatial regression method in realistic conditions.     

A Note on Balanced Sampling and Robust Genetic Predictors

The concept of balanced sampling is applied to prediction in finite samples using model based inference procedures. Necessary and sufficient conditions are derived for a general linear model with arbitrary covariance structure to yield the expansion estimator as the best linear unbiased predictor for the mean. The analysis is extended to produce a robust estimator for the mean squared error under balanced sampling and the results are discussed in the context of statistical genetics where appropriate sampling produces simple efficient and robust genetic predictors free from unnecessary genetic assumptions.     

Conditional Akaike information under generalized linear and proportional hazards mixed models

We study model selection for clustered data, when the focus is on cluster specific inference. Such data are often modelled using random effects, and conditional Akaike information was proposed in Vaida & Blanchard (2005) and used to derive an information criterion under linear mixed models. Here we extend the approach to generalized linear and proportional hazards mixed models. Outside the normal linear mixed models, exact calculations are not available and we resort to asymptotic approximations. In the presence of nuisance parameters, a profile conditional Akaike information is proposed. Bootstrap methods are considered for their potential advantage in finite samples. Simulations show that the performance of the bootstrap and the analytic criteria are comparable, with bootstrap demonstrating some advantages for larger cluster sizes. The proposed criteria are applied to two cancer datasets to select models when the cluster-specific inference is of interest.     

Construction of Predictive Models to Describe Apparent and Complex Viscosity Values of O/W Model System Meat Emulsions Using Adaptive Neuro – Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN)

This paper introduces an adaptive neuro ?C fuzzy inference system (ANFIS) and artificial neural networks (ANN) models to predict the apparent and complex viscosity values of model system meat emulsions. Constructed models were compared with multiple linear regression (MLR) modeling based on their estimation performance. The root mean square error (RMSE), mean absolute error (MAE) and determination coefficient (R ²) statistics were performed to evaluate the accuracy of the models tested. Comparison of the models showed that the ANFIS model performed better than the ANN and MLR models to estimate the apparent and complex viscosity values of the model system meat emulsions. Coefficients of determination (R ²) calculated for estimation performance of ANFIS modeling to predict apparent and complex viscosity of the emulsions were 0.996 and 0.992, respectively. Similar R ² values (0.991 and 0.985) were obtained when estimating the performance of the ANN model. In the present study, use of the constructed ANFIS models can be suggested to effectively predict the apparent and complex viscosity values of model system meat emulsions.     

Soft-sensing method based on FDLS-SVM in marine alkaline protease fermentation process

Abstract

To overcome the problem that soft-sensing model cannot be updated with the bioprocess changes, this article proposed a soft-sensing modeling method which combined fuzzy c-means clustering (FCM) algorithm with least squares support vector machine theory (LS-SVM). FCM is used for separating a whole training data set into several clusters with different centers, each subset is trained by LS-SVM and sub-models are developed to fit different hierarchical property of the process. The new sample data that bring new operation information is introduced in the model, and the fuzzy membership function of the sample to each clustering is first calculated by the FCM algorithm. Then, a corresponding LS-SVM sub-model of the clustering with the largest fuzzy membership function is used for performing dynamic learning so that the model can update online. The proposed method is applied to predict the key biological parameters in the marine alkaline protease MP process. The simulation result indicates that the soft-sensing modeling method increases the model’s adaptive abilities in various operation conditions and can improve its generalization ability.     

Analytical fuzzy approach to biological data analysis

The assessment of the physiological state of an individual requires an objective evaluation of biological data while taking into account both measurement noise and uncertainties arising from individual factors. We suggest to represent multi-dimensional medical data by means of an optimal fuzzy membership function. A carefully designed data model is introduced in a completely deterministic framework where uncertain variables are characterized by fuzzy membership functions. The study derives the analytical expressions of fuzzy membership functions on variables of the multivariate data model by maximizing the over-uncertainties-averaged-log-membership values of data samples around an initial guess. The analytical solution lends itself to a practical modeling algorithm facilitating the data classification. The experiments performed on the heartbeat interval data of 20 subjects verified that the proposed method is competing alternative to typically used pattern recognition and machine learning algorithms.     

Fuzzy C-means method for clustering microarray data

MOTIVATION: Clustering analysis of data from DNA microarray hybridization studies is essential for identifying biologically relevant groups of genes. Partitional clustering methods such as K-means or self-organizing maps assign each gene to a single cluster. However, these methods do not provide information about the influence of a given gene for the overall shape of clusters. Here we apply a fuzzy partitioning method, Fuzzy C-means (FCM), to attribute cluster membership values to genes. RESULTS: A major problem in applying the FCM method for clustering microarray data is the choice of the fuzziness parameter m. We show that the commonly used value m = 2 is not appropriate for some data sets, and that optimal values for m vary widely from one data set to another. We propose an empirical method, based on the distribution of distances between genes in a given data set, to determine an adequate value for m. By setting threshold levels for the membership values, genes which are tigthly associated to a given cluster can be selected. Using a yeast cell cycle data set as an example, we show that this selection increases the overall biological significance of the genes within the cluster. AVAILABILITY: Supplementary text and Matlab functions are available at http://www-igbmc.u-strasbg.fr/fcm/     

Fuzzy—CRI法对砀山酥梨丰产树形结构的探讨

本文根据Fuzzy-CRI近似推理方法,建立了文中图1的Fuzzy推理系统模型,分别对现有不同类型砀山酥梨树形结构的丰产性能作定量研究,并从中得到了一种理想的树形结构模型．针对本文背景,给定了隶属函数与决策函数的表达形式,提出并讨论了一种新的Fuzzy蕴涵算子．     

Open population maximum likelihood spatial capture‐recapture

Open population capture‐recapture models are widely used to estimate population demographics and abundance over time. Bayesian methods exist to incorporate open population modeling with spatial capture‐recapture (SCR), allowing for estimation of the effective area sampled and population density. Here, open population SCR is formulated as a hidden Markov model (HMM), allowing inference by maximum likelihood for both Cormack‐Jolly‐Seber and Jolly‐Seber models, with and without activity center movement. The method is applied to a 12‐year survey of male jaguars (Panthera onca) in the Cockscomb Basin Wildlife Sanctuary, Belize, to estimate survival probability and population abundance over time. For this application, inference is shown to be biased when assuming activity centers are fixed over time, while including a model for activity center movement provides negligible bias and nominal confidence interval coverage, as demonstrated by a simulation study. The HMM approach is compared with Bayesian data augmentation and closed population models for this application. The method is substantially more computationally efficient than the Bayesian approach and provides a lower root‐mean‐square error in predicting population density compared to closed population models.     

Determination of Cup to Disc Ratio Using Unsupervised Machine Learning Techniques for Glaucoma Detection

The cup nerve head, optic cup, optic disc ratio and neural rim configuration are observed as important for detecting glaucoma at an early stage in clinical practice. The main clinical indicator of glaucoma optic cup to disc ratio is currently determined manually by limiting the mass screening was potential. This paper proposes the following methods for an automatic cup to disc ratio determination. In the first part of the work, fundus image of the optic disc region is considered. Clustering means K is used automatically to extract the optic disc whereas K-value is automatically selected by algorithm called hill climbing. The segmented contour of optic cup has been smoothened by two methods namely elliptical fitting and morphological fitting. Cup to disc ratio is calculated for 50 normal images and 50 fundus images of glaucoma patients. Throughout this paper, the same set of images has been used and for these images, the cup to disc ratio values are provided by ophthalmologist which is taken as the gold standard value. The error is calculated with reference to this gold standard value throughout the paper for cup to disc ratio comparison. The mean error of the K-means clustering method for elliptical and morphological fitting is 4.5% and 4.1%, respectively. Since the error is high, fuzzy C-mean clustering has been chosen and the mean error of the method for elliptical and morphological fitting is 3.83% and 3.52%. The error can further be minimized by considering the inter pixel relation. To achieve another algorithm is by Spatially Weighted fuzzy C-means Clustering (SWFCM) is used. The optic disc and optic cup have clustered and segmented by SWFCM Clustering. The SWFCM mean error clustering method for elliptical and morphological fitting is 3.06% and 1.67%, respectively. In this work fundus images were collected from Aravind eye Hospital, Pondicherry.     

Robust model matching control of immune systems under environmental disturbances: dynamic game approach

A robust model matching control of immune response is proposed for therapeutic enhancement to match a prescribed immune response under uncertain initial states and environmental disturbances, including continuous intrusion of exogenous pathogens. The worst-case effect of all possible environmental disturbances and uncertain initial states on the matching for a desired immune response is minimized for the enhanced immune system, i.e. a robust control is designed to track a prescribed immune model response from the minimax matching perspective. This minimax matching problem could herein be transformed to an equivalent dynamic game problem. The exogenous pathogens and environmental disturbances are considered as a player to maximize (worsen) the matching error when the therapeutic control agents are considered as another player to minimize the matching error. Since the innate immune system is highly nonlinear, it is not easy to solve the robust model matching control problem by the nonlinear dynamic game method directly. A fuzzy model is proposed to interpolate several linearized immune systems at different operating points to approximate the innate immune system via smooth fuzzy membership functions. With the help of fuzzy approximation method, the minimax matching control problem of immune systems could be easily solved by the proposed fuzzy dynamic game method via the linear matrix inequality (LMI) technique with the help of Robust Control Toolbox in Matlab. Finally, in silico examples are given to illustrate the design procedure and to confirm the efficiency and efficacy of the proposed method.