Showing papers in "Artificial Intelligence in Engineering in 1999"

TL;DR: The review reveals the tremendous prospect of using neural networks in process control and shows the multilayered neural network as the most popular network for such process control applications and also shows the lack of actual successful online applications at the present time.

TL;DR: A new approach is proposed which combines canonical space transformation (CST) based on Canonical Analysis (CA), with EST for feature extraction, which can be used to reduce data dimensionality and to optimise the class separability of different gait classes simultaneously.

TL;DR: The ANN model is used to forecast the energy requirements of an electric utility and is then compared to time series models, revealing that the ANN produces results that are close to the actual data.

TL;DR: A systematic evaluation procedure for ML is discussed, which will lead to better evaluation of studies, and consequently to improved research and practice in the area of ML in engineering applications.

TL;DR: The genetic algorithm proposed is efficient and fit for larger scale identical parallel machine scheduling problem for minimizing the makespan, the quality of its solution has advantage over heuristic procedure and simulated annealing method.

TL;DR: The article presents the Negative Feedback Artificial Neural Network, shows how it extracts the model behind the data set and discuses the Artificial Neural network’s forecasting abilities.

TL;DR: The use of genetic algorithms (GAs) to train the Elman and Jordan networks for dynamic systems identification is described, which is an efficient, guided, random search procedure which can simultaneously obtain the optimal weights of both the feedforward and feedback connections.

TL;DR: An artificial neural network based model, which employs a pattern discrimination algorithm to recognise unnatural control chart patterns and is capable of superior ARL performance while the type of the unnatural pattern can also be accurately identified.

TL;DR: The main goals are: to efficiently solve classification problems of such a type and to compare different alternatives for Machine Learning.

TL;DR: The Intelligent Forecasters Construction Set is presented, which is a toolset for constructing forecasting applications that supports the intelligent techniques of fuzzy logic, artificial neural networks, knowledge-based and case-based reasoning and more.

TL;DR: Using various graph representations and the theorems and algorithms embedded within them, provides a fruitful source of representations which can form a basis upon which to extend formal theories of reformulation.

TL;DR: A genetic algorithm for adaptive navigation of a robot-like simulated vehicle that evolves feasible paths by performing an adaptive search on populations of candidate actions is presented.

TL;DR: The genetic algorithm takes topologies as individuals of its population, and tries to find optimal ones by mating, mutating and eliminating them, and finds solutions comparable with and in some cases better than those found by existing heuristic algorithms.

TL;DR: Results from fault diagnosis of glutamic acid fermentation process based on the extended Kalman filter and neural network classifier show that the strategy appears to be better suited to diagnose faults of such an industrial process.

TL;DR: Neural networks and genetic algorithms can be used to implement the fitness function and the search method needed to achieve optimum design and the algorithm successfully converges to good solutions.

TL;DR: The results showed that the proposed algorithm can greatly reduce the number of flops required to train the networks and was applied to the sunspot and Mackey–Glass time-series prediction.

TL;DR: A new adaptive fuzzy logic control scheme based on the structure of the self-tuning regulator and employs neural network and genetic algorithm techniques to show the effectiveness of the proposed scheme.

TL;DR: A control algorithm based on neural networks has been applied to a robot arm which has a highly nonlinear structure and has provided satisfactory results.

TL;DR: This article investigates the approximation of the inverse dynamics of unknown plants using a new type of recurrent backpropagation neural network, which makes use of the direct inverse learning scheme to achieve simple and accurate inverse system identification even in the presence of noise.

TL;DR: This paper presents a provably correct, polynomial time, planning tool and its application to a miniature assembly line for toy cars, and is mainly intended for producing plans in error recovery situations.

TL;DR: A planning system which integrates the reinforcement learning method and a neural network approach with the aim to ensure autonomous robot behavior in unpredictable working conditions is outlined and verified on planned and random examples, proving the main advantages of the proposed approach.

TL;DR: The development of a knowledge-based dynamic job-scheduling system in the low-volume/high-variety manufacturing environment which takes into account the influence of many factors such as machine setup times, cell changes, replacement machines and load balancing among machines is reported.

TL;DR: The approach taken in this paper is that the facilitator selects the boundary agent instead of the local agent when input data is closely located at certain border of subregions, even if decision tree yields an incorrect prediction, the performance of the system is less affected by it.

TL;DR: This paper has described, formally, content verification of a specific type of rule-base using a digraph-based modelling approach and demonstrated that problems in therule-base lead to the existence of certain properties in the digraph and various rule- base model representations that have been devised in this work.

TL;DR: This paper demonstrates the use of multiple models in intelligent control systems where models are organised within a model space of three primitive modelling dimensions: precision, scope and generality .

TL;DR: An operator acquires proficiency by working on the same machine over a period of several years; thus he is able to apply adjustments according to its specific characteristics, and it was found that this machine-specific knowledge consists of articulated and unarticulated knowledge.

TL;DR: The LV-control problem in binary distillation columns is addressed and a feedback linearizing-like controller is designed that shows robustness against external disturbances and set-point changes.

TL;DR: A possible method of matching an FTP with a signal is shown, which will enable the detection of profiles of interest on the trace of a physical parameter over time.

TL;DR: This work can be classified into three parts: a multidimensional signal‐noise neural network model for a microwave smallsignal transistor, a computer simulation of the possible performance (F,Vi,Gtmax) triplets, and the performance curves obtained using the relationships among operation conditions f, VCE, and ICE.

TL;DR: It is shown that the best configuration of the algorithm can be data dependent and hence that an ‘intelligent’ optimization system will need to automatically configure itself with the control knowledge appropriate to the problems the user is solving.