Constrained optimization model for the design of an adaptive (X)over-bar chart

An economic-statistical model is developed for variable parameters (VP) (X) over bar charts in which all design parameters vary adaptively, that is, each of the design parameters (sample size, sampling interval and control-limit width) vary as a function of the most recent process information. The cost function due to controlling the process quality through a VP (X) over bar chart is derived. During the optimization of the cost function, constraints are imposed on the expected times to signal when the process is in and out of control. In this way, required statistical properties can be assured. Through a numerical example, the proposed economic-statistical design approach for VP (X) over bar charts is compared to the economic design for VP (X) over bar charts and to the economic-statistical and economic designs for fixed parameters (FP) (X) over bar charts in terms of the operating cost and the expected times to signal. From this example, it is possible to assess the benefits provided by the proposed model. Varying some input parameters, their effect on the optimal cost and on the optimal values of the design parameters was analysed.

An adaptive chart for monitoring the process mean and variance

Traditionally, an (X) over bar chart is used to control the process mean and an R chart is used to control the process variance. However, these charts are not sensitive to small changes in the process parameters. The adaptive ($) over bar and R charts might be considered if the aim is to detect small disturbances. Due to the statistical character of the joint (X) over bar and R charts with fixed or adaptive parameters, they are not reliable in identifing the nature of the disturbance, whether it is one that shifts the process mean, increases the process variance, or leads to a combination of both effects. In practice, the speed with which the control charts detect process changes may be more important than their ability in identifying the nature of the change. Under these circumstances, it seems to be advantageous to consider a single chart, based on only one statistic, to simultaneously monitor the process mean and variance. In this paper, we propose the adaptive non-central chi-square statistic chart. This new chart is more effective than the adaptive (X) over bar and R charts in detecting disturbances that shift the process mean, increase the process variance, or lead to a combination of both effects. Copyright (c) 2006 John Wiley & Sons, Ltd.

An adaptive system based on artificial neural networks for NPK fluid fertilizer application

The application process of fluid fertilizers through variable rates implemented by classical techniques with feedback and conventional equipments can be inefficient or unstable. This paper proposes an open-loop control system based on artificial neural network of the type multilayer perceptron for the identification and control of the fertilizer flow rate. The network training is made by the algorithm of Levenberg-Marquardt with training data obtained from measurements. Preliminary results indicate a fast, stable and low cost control system for precision fanning. Copyright (C) 2000 IFAC.

Adaptive control charts: A Markovian approach for processes subject to independent disturbances

Recent studies have shown that adaptive X control charts are quicker than traditional X charts in detecting small to moderate shifts in a process. In this article, we propose a joint statistical design of adaptive X and R charts having all design parameters varying adaptively. The process is subjected to two independent assignable causes. One cause changes the process mean and the other changes the process variance. However, the occurrence of one kind of assignable cause does not preclude the occurrence of the other. It is assumed that the quality characteristic is normally distributed and the time that the process remains in control has exponential distribution. Performance measures of these adaptive control charts are obtained through a Markov chain approach. (c) 2005 Elsevier B.V. All rights reserved.

Differential vascular adaptive response in spontaneously hypertensive and Wistar rats: Role of nitric oxide, and prehypertensive and hypertensive states

Stress-induced vascular adaptive response in SHR was investigated, focusing on the endothelium. Noradrenaline responses were studied in intact and denuded aortas from 6-week-old (prehypertensive) and 14-week-old (hypertensive) SHR and age-matched Wistar rats submitted or not to acute stress (20-min swimming and I-h immobilization 25 min apart), preceded or not by chronic stress (2 sessions 2 days apart of 1-h day immobilization for 5-consecutive days). Stress did not alter the reactivity of denuded aorta. Moreover, no alteration in the EC50 values was observed after stress exposure. In intact aortas, acute stress-induced hyporeactivity to noradrenaline similar between strains at both age. Chronic stress potentiated this adaptive response in 6- and 14-week-old Wistar but not in 6-week-old SHR, and did not alter the reactivity of 14-week-old SHR. Maximum response (g) in intact aortas [6-week-old: Wistar 3.25 +/- 0.12, Wistar/acute 1.95 +/- 0.12*, Wistar/chronic 1.36 +/- 0.21*(+), SHR 1.75 +/- 0.11, SHR/acute 0.88 +/- 0.08*, SHR/chronic 0.85 +/- 0.05*; 14-week-old: Wistar 3.83 +/- 0.13, Wistar/acute 2.72 +/- 0.13*, Wistar/chronic 1.91 +/- 0.19*', SHR 4.03 +/- 0.17, SHR/acute 2.26 +/- 0.12*, SHR/chronic 4.10 +/- 0.23; inside the same strain: *P < 0.05 relate to non-stressed rat, (+)P < 0.05 related to acute stressed rat; n = 6-18]. Independent of age and strain, L-NAME and endothelium removal abolished the stress-induced aorta hyporeactivity. Conclusion: the vascular adaptive response to stress is impaired in SHR, independently of the hypertensive state. Moreover, this vascular adaptive response is characterized by endothelial nitric oxide-system hyperactivity in both strains. (c) 2006 Elsevier B.V. All rights reserved.

A hierarchy of adaptive (X)over-bar control charts

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

5-Lipoxygenase Deficiency Impairs Innate and Adaptive Immune Responses during Fungal Infection

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Comments, with reply on 'Continuous time relay-controlled model reference adaptive-system' by A. Abdulkareem and R. Nagarajam

An adaptive scheme is shown by the authors of the above paper (ibid. vol. 71, no. 2, pp. 275-276, Feb. 1983) for continuous time model reference adaptive systems (MRAS), where relays replace the usual multipliers in the existing MRAS. The commenter shows an error in the analysis of the hyperstability of the scheme, such that the validity of this configuration becomes an open question.

Another prefiltering algorithm for adaptive IIR filtering

An algorithm for adaptive IIR filtering that uses prefiltering structure in direct form is presented. This structure has an estimation error that is a linear function of the coefficients. This property greatly simplifies the derivation of gradient-based algorithms. Computer simulations show that the proposed structure improves convergence speed.

New approach of induction machine drive using an adaptive digital PID controller with gain planning based on artificial neural networks

The present work introduces a new strategy of induction machines speed adjustment using an adaptive PID (Proportional Integral Derivative) digital controller with gain planning based on the artificial neural networks. This digital controller uses an auxiliary variable to determine the ideal induction machine operating conditions and to establish the closed loop gain of the system. The auxiliary variable value can be estimated from the information stored in a general-purpose artificial neural network based on CMAC (Cerebellar Model Articulation Controller).

Adaptive control charts for attributes

We develop a general model for adaptive c, np, u and p control charts in which one, two or three design parameters (sample size, sampling interval and control limit width) switch between two values, according to the most recent process information. For a given in-control average sampling rate and a given false alarm rate, the adaptive chart detects changes in the process much faster than a chart with fixed parameters. Moreover, this study also offers general guidance on how to choose an effective design.

A fast electric load forecasting using adaptive neural networks

This work presents a procedure for electric load forecasting based on adaptive multilayer feedforward neural networks trained by the Backpropagation algorithm. The neural network architecture is formulated by two parameters, the scaling and translation of the postsynaptic functions at each node, and the use of the gradient-descendent method for the adjustment in an iterative way. Besides, the neural network also uses an adaptive process based on fuzzy logic to adjust the network training rate. This methodology provides an efficient modification of the neural network that results in faster convergence and more precise results, in comparison to the conventional formulation Backpropagation algorithm. The adapting of the training rate is effectuated using the information of the global error and global error variation. After finishing the training, the neural network is capable to forecast the electric load of 24 hours ahead. To illustrate the proposed methodology it is used data from a Brazilian Electric Company. © 2003 IEEE.

Applying a genetic neuro-model reference adaptive controller in drilling optimization

Motivated by rising drilling operation costs, the oil industry has shown a trend toward real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated with parameters modeling. One of the drillbit performance evaluators, the Rate Of Penetration (ROP), has been used as a drilling control parameter. However, relationships between operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on an auto-regressive with extra input signals, or ARX model and on a Genetic Algorithm (GA) to control the ROP. © [2006] IEEE.