Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003

2003 ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Information Processing ◽  
Neural Information ◽  
Neural Information Processing ◽  
Artificial Neural

MATHEMATICAL SUPPORT FOR INDUCTION SOLDERING CONTROL BASED ON INTELLIGENT METHODS OF INFORMATION PROCESSING

2021 ◽  
pp. 81-87
Author(s):  
А.В. Милов
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Information Processing ◽  
Mathematical Models ◽  
Technological Process ◽  
Artificial Neural ◽  
Intelligent Methods

В статье представлены математические модели на основе искусственных нейронных сетей, используемые для управления индукционной пайкой. Обучение искусственных нейронных сетей производилось с использованием многокритериального генетического алгоритма FFGA. This article presents mathematical models based on artificial neural networks used to control induction soldering. The artificial neural networks were trained using the FFGA multicriteria genetic algorithm. The developed models allow to control induction soldering under conditions of incomplete or unreliable information, as well as under conditions of complete absence of information about the technological process.

scholarly journals Advances in verification of ReLU neural networks

2020 ◽  
Author(s):  
Ansgar Rössig ◽  
Milena Petkovic
Keyword(s):  
Neural Networks ◽  
Information Processing ◽  
Computational Study ◽  
Autonomous Driving ◽  
Mixed Integer ◽  
Data Set ◽  
Approximation Techniques ◽  
Neural Information ◽  
Neural Information Processing ◽  
Algorithmic Approaches

Abstract We consider the problem of verifying linear properties of neural networks. Despite their success in many classification and prediction tasks, neural networks may return unexpected results for certain inputs. This is highly problematic with respect to the application of neural networks for safety-critical tasks, e.g. in autonomous driving. We provide an overview of algorithmic approaches that aim to provide formal guarantees on the behaviour of neural networks. Moreover, we present new theoretical results with respect to the approximation of ReLU neural networks. On the other hand, we implement a solver for verification of ReLU neural networks which combines mixed integer programming with specialized branching and approximation techniques. To evaluate its performance, we conduct an extensive computational study. For that we use test instances based on the ACAS Xu system and the MNIST handwritten digit data set. The results indicate that our approach is very competitive with others, i.e. it outperforms the solvers of Bunel et al. (in: Bengio, Wallach, Larochelle, Grauman, Cesa-Bianchi, Garnett (eds) Advances in neural information processing systems (NIPS 2018), 2018) and Reluplex (Katz et al. in: Computer aided verification—29th international conference, CAV 2017, Heidelberg, Germany, July 24–28, 2017, Proceedings, 2017). In comparison to the solvers ReluVal (Wang et al. in: 27th USENIX security symposium (USENIX Security 18), USENIX Association, Baltimore, 2018a) and Neurify (Wang et al. in: 32nd Conference on neural information processing systems (NIPS), Montreal, 2018b), the number of necessary branchings is much smaller. Our solver is publicly available and able to solve the verification problem for instances which do not have independent bounds for each input neuron.

Comprehensive Modelling of ANN

2021 ◽  
pp. 18-29
Author(s):  
Meghna Babubhai Patel ◽  
Jagruti N. Patel ◽  
Upasana M. Bhilota
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Information Processing ◽  
Human Brain ◽  
Processing System ◽  
Novel Structure ◽  
Pros And Cons ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Networks Structure

An artificial neural network (ANN) is an information processing modelling of the human brain inspired by the way biological nervous systems behave. There are about 100 billion neurons in the human brain. Each neuron has a connection point between 1,000 and 100,000. The key element of this paradigm is the novel structure of the information processing system. In the human brain, information is stored in such a way as to be distributed, and we can extract more than one piece of this information when necessary from our memory in parallel. We are not mistaken when we say that a human brain is made up of thousands of very powerful parallel processors. It is composed of a large number of highly interconnected processing elements (neurons) working in union to solve specific problems. ANN, like people, learns by example. The chapter includes characteristics of artificial neural networks, structure of ANN, elements of artificial neural networks, pros and cons of ANN.

scholarly journals Artificial neural networks and the study of evolution of prey coloration

2007 ◽  
Vol 362 (1479) ◽  
pp. 421-430 ◽  
Author(s):  
Sami Merilaita
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Information Processing ◽  
Visual Information ◽  
Visual Complexity ◽  
Network Models ◽  
Neural Network Models ◽  
General Terms ◽  
Artificial Neural

In this paper, I investigate the use of artificial neural networks in the study of prey coloration. I briefly review the anti-predator functions of prey coloration and describe both in general terms and with help of two studies as specific examples the use of neural network models in the research on prey coloration. The first example investigates the effect of visual complexity of background on evolution of camouflage. The second example deals with the evolutionary choice of defence strategy, crypsis or aposematism. I conclude that visual information processing by predators is central in evolution of prey coloration. Therefore, the capability to process patterns as well as to imitate aspects of predator's information processing and responses to visual information makes neural networks a well-suited modelling approach for the study of prey coloration. In addition, their suitability for evolutionary simulations is an advantage when complex or dynamic interactions are modelled. Since not all behaviours of neural network models are necessarily biologically relevant, it is important to validate a neural network model with empirical data. Bringing together knowledge about neural networks with knowledge about topics of prey coloration would provide a potential way to deepen our understanding of the specific appearances of prey coloration.

scholarly journals Artificial neural network for the recognition of human emotions under a backpropagation algorithm

Athenea ◽  
2021 ◽  
Vol 2 (5) ◽  
pp. 29-34
Author(s):  
Alexander Caicedo ◽  
Anthony Caicedo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Information Processing ◽  
Emotion Recognition ◽  
Buenos Aires ◽  
Back Propagation ◽  
Feedforward Neural Networks ◽  
Backpropagation Algorithm ◽  
Neural Information ◽  
Neural Information Processing

The era of the technological revolution increasingly encourages the development of technologies that facilitate in one way or another people's daily activities, thus generating a great advance in information processing. The purpose of this work is to implement a neural network that allows classifying the emotional states of a person based on the different human gestures. A database is used with information on students from the PUCE-E School of Computer Science and Engineering. Said information are images that express the gestures of the students and with which the comparative analysis with the input data is carried out. The environment in which this work converges proposes that the implementation of this project be carried out under the programming of a multilayer neuralnetwork. Multilayer feeding neural networks possess a number of properties that make them particularly suitable for complex pattern classification problems [8]. Back-Propagation [4], which is a backpropagation algorithm used in the Feedforward neural network, was taken into consideration to solve the classification of emotions. Keywords: Image processing, neural networks, gestures, back-propagation, feedforward, classification, emotions. References [1]S. Gangwar, S. Shukla, D. Arora. “Human Emotion Recognition by Using Pattern Recognition Network”, Journal of Engineering Research and Applications, Vol. 3, Issue 5, pp.535-539, 2013. [2]K. Rohit. “Back Propagation Neural Network based Emotion Recognition System”. International Journal of Engineering Trends and Technology (IJETT), Vol. 22, Nº 4, 2015. [3]S. Eishu, K. Ranju, S. Malika, “Speech Emotion Recognition using BFO and BPNN”, International Journal of Advances in Science and Technology (IJAST), ISSN2348-5426, Vol. 2 Issue 3, 2014. [4]A. Fiszelew, R. García-Martínez and T. de Buenos Aires. “Generación automática de redes neuronales con ajuste de parámetros basado en algoritmos genéticos”. Revista del Instituto Tecnológico de Buenos Aires, 26, 76-101, 2002. [5]Y. LeCun, B. Boser, J. Denker, D. Henderson, R. Howard, W. Hubbard, and L. Jackel. “Handwritten digit recognition with a back-propagation network”. In Advances in neural information processing systems. pp. 396-404, 1990. [6]G. Bebis and M. Georgiopoulos. “Feed-forward neural networks”. IEEE Potentials, 13(4), 27-31, 1994. [7]G. Huang, Q. Zhu and C. Siew. “Extreme learning machine: a new learning scheme of feedforward neural networks”. In Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference. Vol. 2, pp. 985-990. IEEE, 2004. [8]D. Montana and L. Davis. “Training Feedforward Neural Networks Using Genetic Algorithms”. In IJCAI, Vol. 89, pp. 762-767, 1989. [9]I. Sutskever, O. Vinyals and Q. Le. “Sequence to sequence learning with neural networks”. In Advances in neural information processing systems. pp. 3104-3112, 2014. [10]J. Schmidhuber. “Deep learning in neural networks: An overview”. Neural networks, 61, 85-117, 2015. [11]R. Santos, M. Ruppb, S. Bonzi and A. Filetia, “Comparación entre redes neuronales feedforward de múltiples capas y una red de función radial para detectar y localizar fugas en tuberías que transportan gas”. Chem. Ing.Trans , 32 (1375), e1380, 2013.

Connectionism

2018 ◽  
Author(s):  
Brian P. McLaughlin
Keyword(s):  
Neural Networks ◽  
Nervous System ◽  
Artificial Neural Networks ◽  
Information Processing ◽  
Potential Role ◽  
Cognitive Architecture ◽  
Process Information ◽  
Computational Theory ◽  
Connectionist Networks ◽  
Artificial Neural

Connectionism is an approach to computation that uses connectionist networks. A connectionist network is composed of information-processing units (or nodes); typically, many units process information simultaneously, giving rise to massively ‘parallel distributed processing’. Units process information only locally: they respond only to their specific input lines by changing or retaining their activation values; and they causally influence the activation values of their output units by transmitting amounts of activation along connections of various weights or strengths. As a result of such local unit processing, networks themselves can behave in rule-like ways to compute functions. The study of connectionist computation has grown rapidly since the early 1980s and now extends to every area of cognitive science. For the philosophy of psychology, the primary interest of connectionist computation is its potential role in the computational theory of cognition – the theory that cognitive processes are computational. Networks are employed in the study of perception, memory, learning and categorization; and it has been claimed that connectionism has the potential to yield an alternative to the classical view of cognition as rule-governed symbol manipulation. Since cognitive capacities are realized in the central nervous system, perhaps the most attractive feature of the connectionist approach to cognitive modelling is the neural-like aspects of network architectures. The members of a certain family of connectionist networks, artificial neural networks, have proved to be a valuable tool for investigating information processing within the nervous system. In artificial neural networks, units are neuron-like; connections, axon-like; and the weights of connections function in ways analogous to synapses. Another attraction is that connectionist networks, with their units sensitive to varying strengths of multiple inputs, carry out in natural ways ‘multiple soft constraint satisfaction’ tasks – assessing the extent to which a number of non-mandatory, weighted constraints are satisfied. Tasks of this sort occur in motor-control, early vision, memory, and in categorization and pattern recognition. Moreover, typical networks can re-programme themselves by adjusting the weights of the connections among their units, thereby engaging in a kind of ‘learning’; and they can do so even on the basis of the sorts of noisy and/or incomplete data people typically encounter. The potential role of connectionist architectures in the computational theory of cognition is, however, an open question. One possibility is that cognitive architecture is a ‘mixed architecture’, with classical and connectionist modules. But the most widely discussed view is that cognitive architecture is thoroughly connectionist. The leading challenge to this view is that an adequate cognitive theory must explain high-level cognitive phenomena such as the systematicity of thought (someone who can think ‘The dog chases the cat’ can also think ‘The cat chases the dog’), its productivity (our ability to think a potential infinity of thoughts) and its inferential coherence (people can infer ‘p’ from ‘p and q’). It has been argued that a connectionist architecture could explain such phenomena only if it implements a classical, language-like symbolic architecture. Whether this is so, however, and, indeed, even whether there are such phenomena to be explained, are currently subjects of intense debate.

scholarly journals Development of a method for assessment and forecasting of the radio electronic environment

2021 ◽  
pp. 30-40
Author(s):  
Oleg Sova ◽  
Andrii Shyshatskyi ◽  
Olha Salnikova ◽  
Oleksandr Zhuk ◽  
Oleksandr Trotsko ◽  
...  
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Artificial Neural Networks ◽  
Information Processing ◽  
System Analysis ◽  
Human Life ◽  
Cognitive Model ◽  
Additional Information ◽  
Electronic Environment ◽  
Artificial Neural

Decision making support systems (DSS) are actively used in all spheres of human life. The system of the electronic environment analysis is not an exception. However, there are a number of problems in the analysis of the electronic environment, for example: the signals are analyzed in a complex electronic environment against the background of intentional and natural interference. Input signals do not match the standards, and their interpretation depends on the experience of the operator (expert), the completeness of additional information on a particular task (uncertainty condition). The best solution in this situation is found in the integration with the data of the information system analysis of the electronic environment, artificial neural networks and fuzzy cognitive models. Their advantages are also the ability to work in real time and quick adaptation to specific situations. The article develops a method for assessing and forecasting the electronic environment. Improving the efficiency of evaluation information processing is achieved through the use of evolving neuro-fuzzy artificial neural networks; learning not only the synaptic weights of the artificial neural network, the type and parameters of the membership function. The efficiency of information processing is also achieved through training in the architecture of artificial neural networks; taking into account the type of uncertainty of the information that has to be assessed; synthesis of rational structure of fuzzy cognitive model. It reduces the computational complexity of decision-making; has no accumulation of learning error of artificial neural networks as a result of processing the information coming to the input of artificial neural networks. The example of assessing the state of the electronic environment showed an increase in the efficiency of assessment at the level of 15–25 % on the efficiency of information processing

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