A hybrid training method for B-spline neural networks

Current and past research has brought up new views related to the optimization of neural networks. For a fixed structure, second order methods are seen as the most promising. From previous works we have shown how second order methods are of easy applicability to a neural network. Namely, we have proved how the Levenberg-Marquard possesses not only better convergence but how it can assure the convergence to a local minima. However, as any gradient-based method, the results obtained depend on the startup point. In this work, a reformulated Evolutionary algorithm - the Bacterial Programming for Levenberg-Marquardt is proposed, as an heuristic which can be used to determine the most suitable starting points, therefore achieving, in most cases, the global optimum.

Completely supervised training algorithms for B-spline neural networks and neuro-fuzzy systems

Complete supervised training algorithms for B-spline neural networks and fuzzy rule-based systems are discussed. By interducing the relationship between B-spline neural networks and certain types of fuzzy models, training algorithms developed initially for neural networks can be adapted by fuzzy systems.

Training multilayer perceptrons - an optimization perspective

Multilayer perceptrons (MLPs) (1) are the most common artificial neural networks employed in a large field of applications. In control and signal processing applications, MLPs are mainly used as nonlinear mapping approximators. The most common training algorithm used with MLPs is the error back-propagation (BP) alg. (1).

Training multilayer perceptrons for control systems applications - a comparison of different approaches

Multilayer perceptrons (MLPs) (1) are the most common artificial neural networks employed in a large field of applications. In control and signal processing applications, MLPs are mainly used as nonlinear mapping approximators. The most common training algorithm used with MLPs is the error back-propagation (BP) alg. (1).

Supervised training approach using spiking neural networks

One of the basic aspects of some neural networks is their attempt to approximate as much as possible their biological counterparts. The goal is to achieve a simple and robust network, easy to understand and able of simulating the human brain at a computational level. Recently a third generation of neural networks (NN) [1], called Spiking Neural Networks(SNN) was appeared. This new kind of networks use the time of a electrical pulse, or spike, to encode the information. In the first and second generation of NN analog values are used in the communication between neurons.

Training neuro-fuzzy models using evolution based algorithms

The normal design process for neural networks or fuzzy systems involve two different phases: the determination of the best topology, which can be seen as a system identification problem, and the determination of its parameters, which can be envisaged as a parameter estimation problem. This latter issue, the determination of the model parameters (linear weights and interior knots) is the simplest task and is usually solved using gradient or hybrid schemes. The former issue, the topology determination, is an extremely complex task, especially if dealing with real-world problems.

Radiographers perceptions of magnetic resonance imaging: a study of the causes that lead to the exams repetition

Purpose: To know how often occur the repetitions of MRI exams and sequences in radiology departments. Methods and Materials: A self applied-questionnaire was used as instrument and assigned to 57 radiographers who performed MRI exams to determine which were the causes that lead to the repetition. The questionnaires were interpreted and statistically analyzed through descriptive statistics and Spearman’s rho correlations. Results: At a 95% confidence interval, the major results suggest that the patient’s movement during de MRI exams is the main cause to repeat this exams (mean of 3.88 on a 5 points likert scale). However, there are causes related to the radiographer’s and the results showed that the introduction of wrong imaging parameters by the performer are a major cause too (N=26). Spearman rho correlations between radiographer’s time of experience and frequency of MRI exams repetitions were poor and not significant (r=0.141; p=0.297). The correlations between radiographer’s tiredness and frequency of MRI exams repetitions were negative, weak and not significant (r= -0.151; p=0.263). Conclusion: The patients’ movement may disrupt the examination or degrade the images with artifacts. The level of experience doesn’t influence the repetitions of MRI exams, it seems that seniors radiographers don’t have improvements in performance as it should be expected. It’s recommendable to do training courses regularly to improve the performance and systematically evaluate. Several features will need to be identified which would decrease the MRI exams repetitions.