Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies

An algorithm that learns from a set of examples should ideally be able to exploit the available resources of (a) abundant computing power and (b) domain-specific knowledge to improve its ability to generalize. Connectionist theory-refinement systems, which use background knowledge to select a neural network's topology and initial weights, have proven to be effective at exploiting domain-specific knowledge; however, most do not exploit available computing power. This weakness occurs because they lack the ability to refine the topology of the neural networks they produce, thereby limiting generalization, especially when given impoverished domain theories. We present the REGENT algorithm which uses (a) domain-specific knowledge to help create an initial population of knowledge-based neural networks and (b) genetic operators of crossover and mutation (specifically designed for knowledge-based networks) to continually search for better network topologies. Experiments on three real-world domains indicate that our new algorithm is able to significantly increase generalization compared to a standard connectionist theory-refinement system, as well as our previous algorithm for growing knowledge-based networks.Comment: See http://www.jair.org/ for any accompanying file

Surrogate Optimization of Deep Neural Networks for Groundwater Predictions

Sustainable management of groundwater resources under changing climatic conditions require an application of reliable and accurate predictions of groundwater levels. Mechanistic multi-scale, multi-physics simulation models are often too hard to use for this purpose, especially for groundwater managers who do not have access to the complex compute resources and data. Therefore, we analyzed the applicability and performance of four modern deep learning computational models for predictions of groundwater levels. We compare three methods for optimizing the models' hyperparameters, including two surrogate model-based algorithms and a random sampling method. The models were tested using predictions of the groundwater level in Butte County, California, USA, taking into account the temporal variability of streamflow, precipitation, and ambient temperature. Our numerical study shows that the optimization of the hyperparameters can lead to reasonably accurate performance of all models (root mean squared errors of groundwater predictions of 2 meters or less), but the ''simplest'' network, namely a multilayer perceptron (MLP) performs overall better for learning and predicting groundwater data than the more advanced long short-term memory or convolutional neural networks in terms of prediction accuracy and time-to-solution, making the MLP a suitable candidate for groundwater prediction.Comment: submitted to Journal of Global Optimization; main paper: 25 pages, 19 figures, 1 table; online supplement: 11 pages, 18 figures, 3 table

Identification of dynamic loads on structural component with artificial neural networks

Enhancing structural components by implementing sensors offers great potential regarding condition monitoring for lifetime analysis, predictive maintenance and automatic adaptation to environmental conditions. This article describes an approach to determining the operational forces applied to the front suspension arm of a car using strain gauges. Since suspension arms are components with free-form surfaces, an analytical calculation of applied forces by means of measured strains is not feasible. Hence, artificial neural networks are applied to approximate the functional relationship. The results reveal how artificial neural networks can be applied to identify load conditions on structural components and, therefore, deliver essential data for condition monitoring

A generic optimising feature extraction method using multiobjective genetic programming

In this paper, we present a generic, optimising feature extraction method using multiobjective genetic programming. We re-examine the feature extraction problem and show that effective feature extraction can significantly enhance the performance of pattern recognition systems with simple classifiers. A framework is presented to evolve optimised feature extractors that transform an input pattern space into a decision space in which maximal class separability is obtained. We have applied this method to real world datasets from the UCI Machine Learning and StatLog databases to verify our approach and compare our proposed method with other reported results. We conclude that our algorithm is able to produce classifiers of superior (or equivalent) performance to the conventional classifiers examined, suggesting removal of the need to exhaustively evaluate a large family of conventional classifiers on any new problem. (C) 2010 Elsevier B.V. All rights reserved

Prediction of Force Measurements of a Microbend Sensor Based on an Artificial Neural Network

Artificial neural network (ANN) based prediction of the response of a microbend fiber optic sensor is presented. To the best of our knowledge no similar work has been previously reported in the literature. Parallel corrugated plates with three deformation cycles, 6 mm thickness of the spacer material and 16 mm mechanical periodicity between deformations were used in the microbend sensor. Multilayer Perceptron (MLP) with different training algorithms, Radial Basis Function (RBF) network and General Regression Neural Network (GRNN) are used as ANN models in this work. All of these models can predict the sensor responses with considerable errors. RBF has the best performance with the smallest mean square error (MSE) values of training and test results. Among the MLP algorithms and GRNN the Levenberg-Marquardt algorithm has good results. These models successfully predict the sensor responses, hence ANNs can be used as useful tool in the design of more robust fiber optic sensors

Estimation of CNC Grinding Process Parameters Using Different Neural Networks

Continuation of research on solving the problem of estimation of CNC grinding process parameters of multi-layer ceramics is presented in the paper. Heuristic analysis of the process was used to define the attributes of influence on the grinding process and the research model was set. For the problem of prediction - estimation of the grinding process parameters the following networks were used in experimental work: Modular Neural Network (MNN), Radial Basis Function Neural Network (RBFNN), General Regression Neural Network (GRNN) and Self-Organizing Map Neural Network (SOMNN). The experimental work, based on real data from the technological process was performed for the purpose of training and testing various architectures and algorithms of neural networks. In the architectures design process different rules of learning and transfer functions and other attributes were used. RMS error was used as a criterion for value evaluation and comparison of the realised neural networks and was compared with previous results obtained by Back-Propagation Neural Network (BPNN). In the validation phase the best results were obtained by Back-Propagation Neural Network (RMSE 12,43 %), Radial Basis Function Neural Network (RMSE 13,24 %,), Self-Organizing Map Neural Network (RMSE 13,38 %) and Modular Neural Network (RMSE 14,45 %). General Regression Neural Network (RMSE 21,78 %) gave the worst results