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PubMed2006

Globally asymptotic stability of a class of neutral-type neural networks with delays.

Several stability conditions for a class of systems with retarded-type delays are presented in the literature. However, no results have yet been presented for neural networks with neutral-type delays. Accordingly, this correspondence investigates the globally asymptotic stability of a class of neutral-type neural networks with delays. This class of systems includes Hopfield neural networks, cellular neural networks, and Cohen-Grossberg neural networks. Based on the Lyapunov stability method, two delay-independent sufficient stability conditions are derived. These stability conditions are easily checked and can be derived from the connection matrix and the network parameters without the requirement for any assumptions regarding the symmetry of the interconnections. Two illustrative examples are presented to demonstrate the validity of the proposed stability criteria.

Algorithms↗
PubMed1996

Sequential use of neural networks for survival prediction in AIDS.

Prognostic assessment of patients is a key part of medical care. Although neural networks can be used to model survival, their accuracy has been limited for a variety of factors, including (1) the lack of data balance in certain intervals and (2) the lack of representation of temporal dependencies in the network architecture. Both problems can be solved with the use of sequential neural networks, which establish predictions for a certain time point and then use these predictions to produce survival estimates for other time points. If the sequence of models is adequate, sequential neural networks produce more accurate estimates of survival than standard neural networks, as shown in this example in the domain of AIDS. Assessments of survival in one, two, three, five and six years become more accurate (as measured by the areas under the ROC curves) when initial predictions of survival in four years are used in a sequential neural network model.

Acquired Immunodeficiency Syndrome↗
PubMed2006

Improved conditions for global exponential stability of recurrent neural networks with time-varying delays.

This paper presents new theoretical results on global exponential stability of recurrent neural networks with bounded activation functions and time-varying delays. The stability conditions depend on external inputs, connection weights, and time delays of recurrent neural networks. Using these results, the global exponential stability of recurrent neural networks can be derived, and the estimated location of the equilibrium point can be obtained. As typical representatives, the Hopfield neural network (HNN) and the cellular neural network (CNN) are examined in detail.

Algorithms↗
PubMed1991

An overview of neural networks.

Some of the world's leading researchers in neural networks submitted their most recent results concerning their research in neural networks to the author for inclusion in this survey. Descriptive accounts of their collective papers are presented as well as a list of sources of information concerning neural networks, such as journals, books, and technical reports. The material is broken into categories related to established areas in computer science, robotics, neural modeling, and engineering.

Algorithms↗
PubMed2006

[Monitoring the depth of anesthesia using a fuzzy neural network based on EEG].

In this paper, a fuzzy neural network (FNN) is proposed for fusing the anesthesia information, and realizing the monitoring of the depth of anesthesia (DOA). EEG data from 31 patients undergoing general anesthesia with different anesthetic agents, and Kc complexity (Kc), approximate entropy (ApEn) were extracted and the fuzzy neural network was trained by 25 samples, and tested by the other 6 samples. The results show that the outputs of the fuzzy neural network whose inputs were Kc and ApEn obtained under the awake state and asleep state, exist obvious difference. It can be regarded as an quantitative index to estimate DOA.

Algorithms↗
PubMed2005

Prediction of molecular-dynamics simulation results using feedforward neural networks: reaction of a C2 dimer with an activated diamond (100) surface.

A new approach involving neural networks combined with molecular dynamics has been used for the determination of reaction probabilities as a function of various input parameters for the reactions associated with the chemical-vapor deposition of carbon dimers on a diamond (100) surface. The data generated by the simulations have been used to train and test neural networks. The probabilities of chemisorption, scattering, and desorption as a function of input parameters, such as rotational energy, translational energy, and direction of the incident velocity vector of the carbon dimer, have been considered. The very good agreement obtained between the predictions of neural networks and those provided by molecular dynamics and the fact that, after training the network, the determination of the interpolated probabilities as a function of various input parameters involves only the evaluation of simple analytical expressions rather than computationally intensive algorithms show that neural networks are extremely powerful tools for interpolating the probabilities and rates of chemical reactions. We also find that a neural network fits the underlying trends in the data rather than the statistical variations present in the molecular-dynamics results. Consequently, neural networks can also provide a computationally convenient means of averaging the statistical variations inherent in molecular-dynamics calculations. In the present case the application of this method is found to reduce the statistical uncertainty in the molecular-dynamics results by about a factor of 3.5.

Journal Article↗
PubMed2005

Development of an inorganic cations retention model in ion chromatography by means of artificial neural networks with different two-phase training algorithms.

This paper describes development of artificial neural network (ANN) retention model, which can be used for method development in variety of ion chromatographic applications. By using developed retention model it is possible both to improve performance characteristic of developed method and to speed up new method development by reducing unnecessary experimentation. Multilayered feed forward neural network has been used to model retention behaviour of void peak, lithium, sodium, ammonium, potassium, magnesium, calcium, strontium and barium in relation with the eluent flow rate and concentration of methasulphonic acid (MSA) in eluent. The probability of finding the global minimum and fast convergence at the same time were enhanced by applying a two-phase training procedure. The developed two-phase training procedure consists of both first and second order training. Several training algorithms were applied and compared, namely: back propagation (BP), delta-bar-delta, quick propagation, conjugate gradient, quasi Newton and Levenberg-Marquardt. It is shown that the optimized two-phase training procedure enables fast convergence and avoids problems arisen from the fact that every new weight initialization can be regarded as a new starting position and yield irreproducible neural network if only second order training is applied. Activation function, number of hidden layer neurons and number of experimental data points used for training set were optimized in order to insure good predictive ability with respect to speeding up retention modelling procedure by reducing unnecessary experimental work. The predictive ability of optimized neural networks retention model was tested by using several statistical tests. This study shows that developed artificial neural network are very accurate and fast retention modelling tool applied to model varied inherent non-linear relationship of retention behaviour with respect to mobile phase parameters.

Algorithms↗
PubMed2004

Generalization of features in the assembly neural networks.

The purpose of the paper is an experimental study of the formation of class descriptions, taking place during learning, in assembly neural networks. The assembly neural network is artificially partitioned into several sub-networks according to the number of classes that the network has to recognize. The features extracted from input data are represented in neural column structures of the sub-networks. Hebbian neural assemblies are formed in the column structure of the sub-networks by weight adaptation. A specific class description is formed in each sub-network of the assembly neural network due to intersections between the neural assemblies. The process of formation of class descriptions in the sub-networks is interpreted as feature generalization. A set of special experiments is performed to study this process, on a task of character recognition using the MNIST database.

Algorithms↗
PubMed1996

Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes.

Artificial neural networks are algorithms that can be used to perform nonlinear statistical modeling and provide a new alternative to logistic regression, the most commonly used method for developing predictive models for dichotomous outcomes in medicine. Neural networks offer a number of advantages, including requiring less formal statistical training, ability to implicitly detect complex nonlinear relationships between dependent and independent variables, ability to detect all possible interactions between predictor variables, and the availability of multiple training algorithms. Disadvantages include its "black box" nature, greater computational burden, proneness to overfitting, and the empirical nature of model development. An overview of the features of neural networks and logistic regression is presented, and the advantages and disadvantages of using this modeling technique are discussed.

Algorithms↗
PubMed2004

Absolute exponential stability of recurrent neural networks with Lipschitz-continuous activation functions and time delays.

This paper investigates the absolute exponential stability of a general class of delayed neural networks, which require the activation functions to be partially Lipschitz continuous and monotone nondecreasing only, but not necessarily differentiable or bounded. Three new sufficient conditions are derived to ascertain whether or not the equilibrium points of the delayed neural networks with additively diagonally stable interconnection matrices are absolutely exponentially stable by using delay Halanay-type inequality and Lyapunov function. The stability criteria are also suitable for delayed optimization neural networks and delayed cellular neural networks whose activation functions are often nondifferentiable or unbounded. The results herein answer a question: if a neural network without any delay is absolutely exponentially stable, then under what additional conditions, the neural networks with delay is also absolutely exponentially stable.

Algorithms↗
PubMed2002

Hierarchical genetic algorithm for near optimal feedforward neural network design.

In this paper, we propose a genetic algorithm based design procedure for a multi layer feed forward neural network. A hierarchical genetic algorithm is used to evolve both the neural networks topology and weighting parameters. Compared with traditional genetic algorithm based designs for neural networks, the hierarchical approach addresses several deficiencies, including a feasibility check highlighted in literature. A multi objective cost function is used herein to optimize the performance and topology of the evolved neural network simultaneously. In the prediction of Mackey Glass chaotic time series, the networks designed by the proposed approach prove to be competitive, or even superior, to traditional learning algorithms for the multi layer Perceptron networks and radial basis function networks. Based upon the chosen cost function, a linear weight combination decision making approach has been applied to derive an approximated Pareto optimal solution set. Therefore, designing a set of neural networks can be considered as solving a two objective optimization problem.

Algorithms↗
PubMed1998

Improved diagnosis of breast implant rupture with sonographic findings and artificial neural networks.

RATIONALE AND OBJECTIVES: The authors evaluated the use of sonographic findings combined with artificial neural networks as an aid to the diagnosis of breast implant rupture. MATERIALS AND METHODS: From a database of 78 breast implants that were evaluated prospectively with sonography and then surgically removed, sonographic findings and surgical results were used to train and test backpropagation and radial basis function artificial neural networks by using the leave-one-out method. Receiver operating characteristic (ROC) curve analysis was used to compare the performance of the different neural networks with that of the radiologists involved. RESULTS: By using the ROC area index as a measure of performance, the artificial neural network (Az = 0.8744) outperformed the radiologists (Az = 0.8057), although not by a statistically significant difference (P = .09). The best-performing network used, in addition to the sonographic findings, the diagnosis of the radiologist as an input. This network (Az = 0.9245) outperformed both the radiologists and the "unaided" networks by a statistically significant margin (P = .02 for radiologists, P = .04 for the unaided network). The network performed remarkably well in those cases in which the radiologists classified the implant as indeterminate, predicting the correct diagnosis in 23 of 25 cases (92%). CONCLUSION: The results suggest that artificial neural networks in tandem with the unaided radiologic diagnosis can improve the accuracy rate in the detection of implant rupture based on sonographic findings. This "team" approach provided the best results.

Breast Implants↗
PubMed2004

Clustering-neural network models for freeway work zone capacity estimation.

Two neural network models, called clustering-RBFNN and clustering-BPNN models, are created for estimating the work zone capacity in a freeway work zone as a function of seventeen different factors through judicious integration of the subtractive clustering approach with the radial basis function (RBF) and the backpropagation (BP) neural network models. The clustering-RBFNN model has the attractive characteristics of training stability, accuracy, and quick convergence. The results of validation indicate that the work zone capacity can be estimated by clustering-neural network models in general with an error of less than 10%, even with limited data available to train the models. The clustering-RBFNN model is used to study several main factors affecting work zone capacity. The results of such parametric studies can assist work zone engineers and highway agencies to create effective traffic management plans (TMP) for work zones quantitatively and objectively.

Cluster Analysis↗
PubMed2006

Improving signal prediction performance of neural networks through multiresolution learning approach.

In this paper, we extend the original work on multiresolution learning for neural networks, and present new developments on the multiresolution learning paradigm. The contributions of this paper include: (1) proposing a new concept and method of adjustable neural activation functions in multiresolution learning to improve neural network learning efficacy and generalization performance for signal predictions; (2) providing new insightful explanations for the multiresolution learning paradigm from a multiresolution optimization perspective; (3) exploring underlying ideas why the multiresolution learning scheme associated with adjustable activation functions would be more appropriate for the multiresolution learning paradigm; and (4) providing rigorous validations to evaluate the multiresolution learning paradigm with adjustable activation functions and comparing it with the schemes of multiresolution learning with fixed activation functions and traditional learning. This paper presents systematically new analytical and experimental results on the multiresolution learning approach for training an individual neural network model, demonstrates our integral solution on neural network learning efficacy, and illustrates the significant improvements on neural networks' generalization performance and robustness for nonlinear signal predictions.

Algorithms↗
PubMed2005

Feedforward neural network models for handling class overlap and class imbalance.

This paper proposes a framework for training feedforward neural network models capable of handling class overlap and imbalance by minimizing an error function that compensates for such imperfections of the training set. A special case of the proposed error function can be used for training variance-controlled neural networks (VCNNs), which are developed to handle class overlap by minimizing an error function involving the class-specific variance (CSV) computed at their outputs. Another special case of the proposed error function can be used for training class-balancing neural networks (CBNNs), which are developed to handle class imbalance by relying on class-specific correction (CSC). VCNNs and CBNNs are compared with conventional feedforward neural networks (FFNNs), quantum neural networks (QNNs), and resampling techniques. The properties of VCNNs and CBNNs are illustrated by experiments on artificial data. Various experiments involving real-world data reveal the advantages offered by VCNNs and CBNNs in the presence of class overlap and class imbalance.

Artifacts↗
PubMed1999

Neural networks for the biochemical prediction of bone mass loss.

Neural networks are specialized artificial intelligence techniques that have shown high efficiency in dealing with complex problems. Paradigms such as backpropagation have been successfully applied in a number of biomedical applications, but not in attempts to identify women at risk of postmenopausal osteoporotic complications. In this paper, several neural networks were trained using different combinations of biochemical variables as inputs. Bone densitometric measurements in Ward's triangle and in the spinal column were used as separate classification criteria (outputs) between slow and fast bone mass losers. The most parsimonious model with the best performance included plasma concentrations of estrone, estradiol, osteocalcin, parathyrin and urine concentrations of calcium and hydroxyproline (expressed as ratio to creatinine excretion) as input neurons; ten neurons in a single hidden layer; and one neuron in the output layer. Diagnostic efficiency was 76% in Ward's triangle and 74% in the spinal column; sensitivity was 70 and 81%, and specificity was 77 and 65%, respectively. Linear discriminant analysis showed a diagnostic efficiency of 66% in Ward's triangle and 64% in the spinal column, sensitivity was 55 and 86%, and specificity was 75 and 13%, respectively. We conclude that performance of the stepwise discriminant analysis was not superior to the neural networks.

Adult↗
PubMed2004

An adaptive Hinfinity controller design for bank-to-turn missiles using ridge Gaussian neural networks.

A new autopilot design for bank-to-turn (BTT) missiles is presented. In the design of autopilot, a ridge Gaussian neural network with local learning capability and fewer tuning parameters than Gaussian neural networks is proposed to model the controlled nonlinear systems. We prove that the proposed ridge Gaussian neural network, which can be a universal approximator, equals the expansions of rotated and scaled Gaussian functions. Although ridge Gaussian neural networks can approximate the nonlinear and complex systems accurately, the small approximation errors may affect the tracking performance significantly. Therefore, by employing the Hinfinity control theory, it is easy to attenuate the effects of the approximation errors of the ridge Gaussian neural networks to a prescribed level. Computer simulation results confirm the effectiveness of the proposed ridge Gaussian neural networks-based autopilot with Hinfinity stabilization.

Algorithms↗
PubMed1990

Holography in artificial neural networks.

The dense interconnections that characterize neural networks are most readily implemented using optical signal processing. Optoelectronic 'neurons' fabricated from semiconducting materials can be connected by holographic images recorded in photorefractive crystals. Processes such as learning can be demonstrated using holographic optical neural networks.

Algorithms↗