PNNARMA model: an alternative to phenomenological models in chemical reactors

This paper is focused on the development of non-linear neural models able to provide appropriate predictions when acting as process simulators. Parallel identification models can be used for this purpose. However, in this work it is shown that since the parameters of parallel identification models are estimated using multilayer feed-forward networks, the approximation of dynamic systems could be not suitable. The solution proposed in this work consists of building up parallel models using a particular recurrent neural network. This network allows to identify the parameter sets of the parallel model in order to generate process simulators. Hence, it is possible to guarantee better dynamic predictions. The dynamic behaviour of the heat transfer fluid temperature in a jacketed chemical reactor has been selected as a case study. The results suggest that parallel models based on the recurrent neural network proposed in this work can be seen as an alternative to phenomenological models for simulating the dynamic behaviour of the heating/cooling circuits.Publicad

Evaluation of Manufactured Product Performance Using Neural Networks

This paper discusses some of the several successful applications of neural networks which have made them a useful simulation tool. After several years of neglect, confidence in the accuracy of neural networks began to grow from the 1980s with applications in power, control and instrumentation and robotics to mention a few. Several successful industrial implementations of neural networks in the field of electrical engineering will be reviewed and results of the authors’ research in the areas of food security and health will also be presented. The research results will show that successful neural simulation results using Neurosolutions software also translated to successful realtime implementation of cost-effective products with reliable overall performance of up to 90%

Modelling, simulation and proportional integral control of a pneumatic motor

Researchers have shown a considerable amount of interest in the control of pneumatic drives over the past decade, for two main reasons, firstly, the response of the system is very slow and it is difficult to attain set points due to hysteresis and secondly, the dynamic model of the system is highly non-linear, which greatly complicates controller design and development. To address these problems, two streams of research effort have evolved and these are: (i) using conventional methods to develop a modelling and control strategy, (ii) adopting a strategy that does not require mathematical model of the system. This paper presents an investigation into the modelling and control of an air motor incorporating a pneumatic equivalent of the electric H-bridge. The pneumatic H-bridge has been devised for speed and direction control of the motor. The system characteristics are divided into three regions, namely low speed, medium speed and high speed. The system is highly nonlinear in the low speed region, for which neuro-modelling, simulation and control strategies are developed

Principles for Consciousness in Integrated Cognitive Control

In this article we will argue that given certain conditions for the evolution of bi- \ud ological controllers, these will necessarily evolve in the direction of incorporating \ud consciousness capabilities. We will also see what are the necessary mechanics for \ud the provision of these capabilities and extrapolate this vision to the world of artifi- \ud cial systems postulating seven design principles for conscious systems. This article \ud was published in the journal Neural Networks special issue on brain and conscious- \ud ness

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

The development of numerical cognition in children and artificial systems: a review of the current knowledge and proposals for multi-disciplinary research

Numerical cognition is a distinctive component of human intelligence such that the observation of its practice provides a window into high-level brain function. The modelling of numerical abilities in artificial cognitive systems can help to confirm existing child development hypotheses and define new ones by means of computational simulations. Meanwhile, new research will help to discover innovative principles for the design of artificial agents with advanced reasoning capabilities and clarify the underlying algorithms (e.g. deep learning) that can be highly effective but difficult to understand for humans. This article promotes new investigation by providing a common resource for researchers with different backgrounds, including computer science, robotics, neuroscience, psychology, and education, who are interested in pursuing scientific collaboration on mutually stimulating research on this topic. The article emphasises the fundamental role of embodiment in the initial development of numerical cognition in children. This strong relationship with the body motivates the Cognitive Developmental Robotics (CDR) approach for new research that can (among others) help to standardise data collection and provide open databases for benchmarking computational models. Furthermore, we discuss the potential application of robots in classrooms and argue that the CDR approach can be extended to assist educators and favour mathematical education

Deep Learning of Inflection and the Cell-Filling Problem

Machine learning offers two basic strategies for morphology induction: lexical segmentation and surface word relation. The first approach assumes that words can be segmented into morphemes. Inferring a novel inflected form requires identification of morphemic constituents and a strategy for their recombination. The second approach dispenses with segmentation: lexical representations form part of a network of associatively related inflected forms. Production of a novel form consists in filling in one empty node in the network. Here, we present the results of a task of word inflection by a recurrent LSTM network that learns to fill in paradigm cells of incomplete verb paradigms. Although the task does not require morpheme segmentation, we show that accuracy in carrying out the inflection task is a function of the model’s sensitivity to paradigm distribution and morphological structure