A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification

In this paper, a two-stage pattern classification and rule extraction system is proposed. The first stage consists of a modified fuzzy min-max (FMM) neural-network-based pattern classifier, while the second stage consists of a genetic-algorithm (GA)-based rule extractor. Fuzzy if-then rules are extracted from the modified FMM classifier, and a ??don't care?? approach is adopted by the GA rule extractor to minimize the number of features in the extracted rules. Five benchmark problems and a real medical diagnosis task are used to empirically evaluate the effectiveness of the proposed FMM-GA system. The results are analyzed and compared with other published results. In addition, the bootstrap hypothesis analysis is conducted to quantify the results of the medical diagnosis task statistically. The outcomes reveal the efficacy of FMM-GA in extracting a set of compact and yet easily comprehensible rules while maintaining a high classification performance for tackling pattern classification tasks.

Authenticating the identity of computer users with typing biometrics and the fuzzy min-max neural network

In this paper, typing biometrics is applied as an additional security measure to the password-based or Personal Identification Number (PIN)-based systems to authenticate the identity of computer users. In particular, keystroke pressure and latency signals are analyzed using the Fuzzy Min-Max (FMM) neural network for authentication purposes. A special pressure-sensitive keyboard is designed to collect keystroke pressure signals, in addition to the latency signals, from computer users when they type their passwords. Based on the keystroke pressure and latency signals, the FMM network is employed to classify the computer users into two categories, i.e., genuine users or impostors. To assess the effectiveness of the proposed approach, two sets of experiments are conducted, and the results are compared with those from statistical methods and neural network models. The experimental outcomes positively demonstrate the potentials of using typing biometrics and the FMM network to provide an additional security layer for the current password-based or PIN-based methods in authenticating the identity of computer users.

An adaptive fuzzy min-max conflict-resolving classifier

This paper describes a novel adaptive network, which agglomerates a procedure based on the fuzzy min-max clustering method, a supervised ART (Adaptive Resonance Theory) neural network, and a constructive conflict-resolving algorithm, for pattern classification. The proposed classifier is a fusion of the ordering algorithm, Fuzzy ARTMAP (FAM) and the Dynamic Decay Adjustment (DDA) algorithm. The network, called Ordered FAMDDA, inherits the benefits of the trio, viz . an ability to identify a fixed order of training pattern presentation for good generalisation; stable and incrementally learning architecture; and dynamic width adjustment of the weights of hidden nodes of conflicting classes. Classification performance of the Ordered FAMDDA is assessed using two benchmark datasets. The performances are analysed and compared with those from FAM and Ordered FAM. The results indicate that the Ordered FAMDDA classifier performs at least as good as the mentioned networks. The proposed Ordered FAMDDA network is then applied to a condition monitoring problem in a power generation station. The process under scrutiny is the Circulating Water (CW) system, with prime attention to condition monitoring of the heat transfer efficiency of the condensers. The results and their implications are analysed and discussed.

Boosting the performance of the fuzzy min-max neural network in pattern classification tasks

In this paper, a boosted Fuzzy Min-Max Neural Network (FMM) is proposed. While FMM is a learning algorithm which is able to learn new classes and to refine existing classes incrementally, boosting is a general method for improving accuracy of any learning algorithm. In this work, AdaBoost is applied to improve the performance of FMM when its classification results deteriorate from a perfect score. Two benchmark databases are used to assess the applicability of boosted FMM, and the results are compared with those from other approaches. In addition, a medical diagnosis task is employed to assess the effectiveness of boosted FMM in a real application. All the experimental results consistently demonstrate that the performance of FMM can be considerably improved when boosting is deployed.

The Max Bell Gallery (Gallery X series) Bishop and Reis

 An interactive exhibition, which recreates the Max Gallery and re-interprets two of Geelong's most renowned paintings (View of Geelong and A Bush Burial) through digital animation

Teaching or preaching — Max Charlesworth and religious education

In this essay I elaborate on the theoretical framework – that of Millian liberalism – that Max Charlesworth brought to many public issues, including that of the relation between education and religion. I will then apply this framework to a debate in which I have been recently involved myself: a debate around the provision of religious instruction in public schools. In the first section I expound Charlesworth’s rejection of secularism in education in a liberal pluralist state and his defence of faith-based schooling. In the second section I uncover the religious motivations behind the Victorian government’s 1950 amendments to the apparently secularist Victorian Education Act of 1872. In section three, I explore the notion of secularism more fully and suggest that the struggle between those who espouse religious instruction in state schools and those who oppose it while advocating a more general form of education about religion is a symptom of a deeper tension between liberalism and communitarianism within the culture of modernist, liberal states.

Application of the fuzzy min-max neural network to fault detection and diagnosis of induction motors

In this paper, an application of the motor current signature analysis (MCSA) method and the fuzzy min–max (FMM) neural network to detection and classification of induction motor faults is described. The finite element method is employed to generate simulated data pertaining to changes in the stator current signatures under different motor conditions. The MCSA method is then used to process the stator current signatures. Specifically, the power spectral density is employed to extract harmonics features for fault detection and classification with the FMM network. Various types of induction motor faults, which include stator winding faults and eccentricity problems, under different load conditions are experimented. The results are analyzed and compared with those from other methods. The outcomes indicate that the proposed technique is effective for fault detection and diagnosis of induction motors under different conditions.

Transfer learning using the online Fuzzy Min-Max neural network

In this paper, we present an empirical analysis on transfer learning using the Fuzzy Min–Max (FMM) neural network with an online learning strategy. Three transfer learning benchmark data sets, i.e., 20 Newsgroups, WiFi Time, and Botswana, are used for evaluation. In addition, the data samples are corrupted with white Gaussian noise up to 50 %, in order to assess the robustness of the online FMM network in handling noisy transfer learning tasks. The results are analyzed and compared with those from other methods. The outcomes indicate that the online FMM network is effective for undertaking transfer learning tasks in noisy environments.