Adaptive Neural Network Training Research Articles

Particle filtering‐based adaptive training of neural networks for real‐time structural damage diagnosis and prognosis

Real-time failure diagnosis and prognosis play key roles in prognostics and health management of critical engineering assets. Physics-based (or empirical–phenomenological) and data-driven methods have been proposed in the literature to solve damage diagnosis and prognosis challenges. Among data-driven methods, neural networks have often successfully proven their regression capabilities for the complex, nonlinear problems typical of this field research field. Indeed, the need to resort to machine learning techniques (e.g., neural networks) becomes stronger when physics-based (or empirical–phenomenological) models are lacking for describing the degradation behavior of the systems under consideration. In this work, we propose to exploit the flexibility offered by neural networks in order to adaptively, and in real time, learn from the monitored structure and derive models for diagnosis and prognosis of structural components subject to damage processes. To this aim, neural networks are embedded within a particle filtering scheme, and the training of the network is performed sequentially and in real time, as observations become available during the damage evolution. The structural health monitoring tool is capable of sequentially updating itself using the observations gathered during the system's operation, avoiding the typical limitations associated to off-line training schemes, which are, in general, not capable of capturing possible changes in the dynamical degradation behavior, when not properly included in the training patterns. For validation and comparison purposes, the method is demonstrated on simulated fatigue crack growths and real crack evolution dynamics observed in a metallic aeronautical panel loaded with a changing-amplitude fatigue load, during an accelerated laboratory test.

Adaptive near minimum error rate training for neural networks with application to multiuser detection in CDMA communication systems

Adaptive training of neural networks is typically done using some stochastic gradient algorithm that aims to minimize the mean square error (MSE). For many classification applications, such as channel equalization and code-division multiple-access (CDMA) multiuser detection, the goal is to minimize the error probability. For these applications, adopting the MSE criterion may lead to a poor performance. A nonlinear adaptive near minimum error rate algorithm called the nonlinear least bit error rate (NLBER) is developed for training neural networks for these kinds of applications. The proposed method is applied to downlink multiuser detection in CDMA communication systems. Simulation results show that the NLBER algorithm has a good convergence speed and a small-size radial basis function network trained by this adaptive algorithm can closely match the performance of the optimal Bayesian multiuser detector. The results also confirm that training the neural network multiuser detector using the least mean square algorithm, although generally converging well in the MSE, can produce a poor error rate performance.

Adaptive Training of Neural Networks for Automatic Seismic Phase Identification

Adaptive Training of Neural Networks for Automatic Seismic Phase Identification

Short term load forecasting using a multilayer neural network with an adaptive learning algorithm

A multilayer feedforward neural network is proposed for short-term load forecasting. To speed up the training process, a learning algorithm for the adaptive training of neural networks is presented. The effectiveness of the neural network with the proposed adaptive learning algorithm is demonstrated by short-term load forecasting of the Taiwan power system. It is found that, once trained by the proposed learning algorithm, the neural network can yield the desired hourly load forecast efficiently and accurately. The proposed adaptive learning algorithm converges much faster than the conventional backpropagation-momentum learning method.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>