Hierarchical neural networks

Mavrovouniotis, Michael L.; Chang, S.

doi:10.1016/0098-1354(92)80053-c

Cited by 84 publications

(31 citation statements)

References 9 publications

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“…Moreover, from an identi cation point of view, the nonlinear parameterization of many neural network based model representations is a serious drawback, in particular as long as there exists powerful linearly parameterized alternative model structures. Recently, there has been some interest in the application a prior knowledge for structuring neural nets (Mavrovouniotis and Chang 1992), initialization of neural network parameters and interpretation of the resulting model through linearizations (Scott and Ray 1993). A further step was taken in (Kramer et al 1992, Thompson and Kramer 1994, Su et al 1992, Psichogios and Ungar 1992, Aoyama and Venkatasubramanian 1993, Brown, Ruchti and Feng 1993 where certain combinations of neural network structures and mechanistic model structures was suggested.…”

Section: Discussionmentioning

confidence: 99%

Operating regime based process modeling and identification

Johansen

Foss

1997

Computers & Chemical Engineering

136

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SummaryThe development of mathematical models is a major bottleneck for the application of advanced model-based techniques for control, optimization, scheduling, automatic fault detection and diagnosis in the process industries. Hence, there is a potential for improved product quality and pro tability, a s w ell as improved production exibility and safety if the cost of model development can be reduced. In this thesis we address some aspects of non-linear modeling and identi cation using a combination of empirical process data and prior knowledge. The major part of this thesis is concerned with a modeling framework based on an operating regime decomposition of the system's operating range. Within each operating regime, the system is modeled with a simple local model. The local models are patched together using a smooth interpolation technique. This framework supports the development of transparent semi-empirical or semi-mechanistic models, and is exible with respect to the prior knowledge and empirical data required. We argue that the cost of model development c a n b e l o w, yet the quality of the model can be high, through the application of this modeling framework, in some cases. These cases are characterized by a limited amount of process knowledge, and the availability of a reasonable amount of process data. Identi cation of model structure and parameters on the basis of process data in this framework is discussed in detail. The properties of the modeling framework is illustred with some semi-realistic examples, both simulated and experimental. In addition, the applicability of the modeling framework for model based control is investigated both through simulation examples and analysis. A minor part of this thesis is an optimization formulation of the modeling and identi cation problem, where the idea is to minimize a criterion that penalizes mismatch b e t ween model behavior and empirical data, and inconsistency with the prior knowledge. The motivation is that in some cases, the application of prior knowledge may be simpler and more transparent than through the direct speci cation of a parameterized model structure, which is the traditional approach. In addition, this thesis contains some theoretical results of more general interest. Some of these results are related to properties of some model structure identi cation criteria, while others are related to the stability and robustness of adaptive control loops based on non-linear models.iii iv

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Section: Discussionmentioning

confidence: 99%

Operating regime based process modeling and identification

Johansen

Foss

1997

Computers & Chemical Engineering

136

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“…Each "neuron" or unit carries out a very simple operation on its inputs and transfers the output to a subsequent node or nodes in the network topology (Specht, 1991). Neural networks exhibit polymorphism in structure and parallelism in computation (Mavrovouniotis & Chang, 1992), and it can be represented as a highly interconnected structure of processing elements with parallel computation capabilities (Grossberg, 1980(Grossberg, , 1982Rumelhart, Hinton, & Williams, 1986;Rumelhart, McClelland, & the PDP research group, 1986). In general, an ANN consists of an input layer (which can be considered the independent variables), one or more hidden layers, and an output layer that is comparable to a categorical dependent variable Garson, 1998).…”

Section: Artificial Neural Network and Performancementioning

confidence: 99%

Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks

Musso

Kyndt

Cascallar

et al. 2013

FLR

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“…Each neuron carries out a very simple operation on its inputs and transfers the output to a subsequent node or nodes in the network topology [89]. Neural networks exhibit polymorphism in structure and parallelism in computation [90], and it can be construed as a highly connected structure of processing elements that attempts to mimic the parallel computation ability of the biological brain [91][92][93][94].…”

Section: Neural Network and Performancementioning

confidence: 99%

Predicting Mathematical Performance: The Effect of Cognitive Processes and Self-Regulation Factors

Musso

Kyndt

Cascallar

et al. 2012

Education Research International

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A substantial number of research studies have investigated the separate influence of working memory, attention, motivation, and learning strategies on mathematical performance and self-regulation in general. There is still little understanding of their impact on performance when taken together, understanding their interactions, and how much each of them contributes to the prediction of mathematical performance. With the emergence of new methodologies and technologies, such as the modelling with predictive systems, it is now possible to study these effects with approaches which use a wide range of data, including student characteristics, to estimate future performance without the need of traditional testing ). This research examines the different cognitive patterns and complex relations between cognitive variables, motivation, and background variables associated with different levels of mathematical performance using artificial neural networks (ANNs). A sample of 800 entering university students was used to develop three ANN models to identify the expected future level of performance in a mathematics test. These ANN models achieved high degree of precision in the correct classification of future levels of performance, showing differences in the pattern of relative predictive weight amongst those variables. The impact on educational quality, improvement, and accountability is highlighted.

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Hierarchical neural networks

Cited by 84 publications

References 9 publications

Operating regime based process modeling and identification

Operating regime based process modeling and identification

Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks

Predicting Mathematical Performance: The Effect of Cognitive Processes and Self-Regulation Factors

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