Closely related species may be very difficult to distinguish morphologically, yet sometimes morphology is the only reasonable possibility for taxonomic classification. Here we present learning-vector-quantization arti...
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Closely related species may be very difficult to distinguish morphologically, yet sometimes morphology is the only reasonable possibility for taxonomic classification. Here we present learning-vector-quantization artificial neural networks as a powerful tool to classify specimens on the basis of geometric morphometric shape measurements. As an example, we trained a neural network to distinguish between field and root voles from Procrustes transformed landmark coordinates on the dorsal side of the skull, which is so similar in these two species that the human eye cannot make this distinction. Properly trained neural networks misclassified only 3% of specimens. Therefore, we conclude that the capacity of learningvectorquantization neural networks to analyse spatial coordinates is a powerful tool among the range of pattern recognition procedures that is available to employ the information content of geometric morphometrics.
Identification of unnatural control chart patterns (CCPs) from manufacturing process measurements is a critical task in quality control as these patterns indicate that the manufacturing process is out-of-control. Rece...
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Identification of unnatural control chart patterns (CCPs) from manufacturing process measurements is a critical task in quality control as these patterns indicate that the manufacturing process is out-of-control. Recently, there have been numerous efforts in developing pattern recognition and classification methods based on artificial neural network to automatically recognize unnatural patterns. Most of them assume that a single type of unnatural pattern exists in process data. Due to this restrictive assumption, severe performance degradations are observed in these methods when unnatural concurrent CCPs present in process data. To address this problem, this paper proposes a novel approach based on singular spectrum analysis (SSA) and learningvectorquantization network to identify concurrent CCPs. The main advantage of the proposed method is that it can be applied to the identification of concurrent CCPs in univariate manufacturing processes. Moreover, there are no permutation and scaling ambiguities in the CCPs recovered by the SSA. These desirable features make the proposed algorithm an attractive alternative for the identification of concurrent CCPs. Computer simulations and a real application for aluminium smelting processes confirm the superior performance of proposed algorithm for sets of typical concurrent CCPs.
In this paper, a mathematical analysis of a class of learningvectorquantization (LVQ) algorithms is presented, Using an appropriate time-coordinate transformation, we show that the LVQ algorithms under consideration...
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In this paper, a mathematical analysis of a class of learningvectorquantization (LVQ) algorithms is presented, Using an appropriate time-coordinate transformation, we show that the LVQ algorithms under consideration can be transformed into linear time-varying stochastic difference equations. Using this fact, we apply stochastic Lyapunov stability arguments, and we prove that the LVQ algorithms under consideration do indeed converge, provided that some appropriate conditions hold.
This paper presents a novel approach to simulate a Knowledge Based System for diagnosis of Breast Cancer using Soft Computing tools like Artificial Neural networks (ANNs) and Neuro Fuzzy Systems. The feed-forward neur...
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ISBN:
(纸本)9781424429271
This paper presents a novel approach to simulate a Knowledge Based System for diagnosis of Breast Cancer using Soft Computing tools like Artificial Neural networks (ANNs) and Neuro Fuzzy Systems. The feed-forward neural network has been trained using three ANN algorithms, the Back propagation algorithm (BPA), the Radial Basis Function (RBF) networks and the learningvectorquantization (LVQ) networks;and also by Adaptive Neuro Fuzzy Inference System (ANFIS). The simulator has been developed using MATLAB and performance is compared by considering the metrics like accuracy of diagnosis, training time, number of neurons, number of epochs etc. The simulation results show that this Knowledge Based Approach can be effectively used for early detection of Breast Cancer to help oncologists to enhance the survival rates significantly.
A major problem in medical science is attaining the correct diagnosis of disease in precedence of its treatment. This paper presents the diagnosis of thyroid disorders using Artificial Neural networks (ANNs). The feed...
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ISBN:
(纸本)9781424429271
A major problem in medical science is attaining the correct diagnosis of disease in precedence of its treatment. This paper presents the diagnosis of thyroid disorders using Artificial Neural networks (ANNs). The feed-forward neural network has been trained using three ANN algorithms;the Back propagation algorithm (BPA), the Radial Basis Function (RBF) networks and the learningvectorquantization (LVQ) networks. The networks are simulated using MATLAB and their performance is assessed in terms of factors like accuracy of diagnosis and training time. The performance comparison helps to find out the best model for diagnosis of thyroid disorders.
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