# Neural networks and artificial intelligence

01 Feb 1989-Vol. 21, Iss: 1, pp 241-245

TL;DR: This paper contends that neural networks must be a significant part of any artificial intelligence course, and illustrates how neural network concepts can be integrated into traditional artificial Intelligence course material.

Abstract: Neural networks have been called “more important than the atomic bomb” and have received a major funding commitment from DARPA. Nevertheless, it is difficult to find even a mention of neural network concepts and applications in many computer science or information systems curricula. In fact, few computer science or information systems faculty are aware of the profound implications of neurocomputing on the future of their field. This paper contends that neural networks must be a significant part of any artificial intelligence course. It illustrates how neural network concepts can be integrated into traditional artificial intelligence course material. Two programming packages for simulating neural networks on personal computers are recommended.

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TL;DR: Cluster-based under-sampling approaches for selecting the representative data as training data to improve the classification accuracy for minority class are proposed and the experimental results show that these approaches outperform the other under-Sampling techniques in the previous studies.

Abstract: For classification problem, the training data will significantly influence the classification accuracy. However, the data in real-world applications often are imbalanced class distribution, that is, most of the data are in majority class and little data are in minority class. In this case, if all the data are used to be the training data, the classifier tends to predict that most of the incoming data belongs to the majority class. Hence, it is important to select the suitable training data for classification in the imbalanced class distribution problem. In this paper, we propose cluster-based under-sampling approaches for selecting the representative data as training data to improve the classification accuracy for minority class and investigate the effect of under-sampling methods in the imbalanced class distribution environment. The experimental results show that our cluster-based under-sampling approaches outperform the other under-sampling techniques in the previous studies.

581 citations

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TL;DR: The inclusion of the concepts and databases related to the intracellular and intercellular bone cell mechanisms and processes of mechanotransduction and the organization of bone as a biologic connected cellular network permit revision of the functional matrix hypothesis.

Abstract: Intercellular gap junctions permit bone cells to intercellularly transmit, and subsequently process, periosteal functional matrix information, after its initial intracellular mechanotransduction. In addition, gap junctions, as electrical synapses, underlie the organization of bone tissue as a connected cellular network, and the fact that all bone adaptation processes are multicellular. The structural and operational characteristics of such biologic networks are outlined and their specific bone cell attributes described. Specifically, bone is "tuned" to the precise frequencies of skeletal muscle activity. The inclusion of the concepts and databases that are related to the intracellular and intercellular bone cell mechanisms and processes of mechanotransduction and the organization of bone as a biologic connected cellular network permit revision of the functional matrix hypothesis, which offers an explanatory chain, extending from the epigenetic event of muscle contraction hierarchically downward to the regulation of the bone cell genome.

163 citations

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01 Nov 2007TL;DR: A set of accurate single carrier and multicarrier fitness functions for the GA implementation that completely control the evolution of the algorithm have been derived and the performance analysis results illustrate the trade-offs between the convergence time of the GA and the size of theGA search space.

Abstract: This paper presents a genetic-algorithm driven, cognitive radio decision engine that determines the optimal radio transmission parameters for single and multicarrier systems Determining the appropriate radio parameters, given a dynamic wireless channel environment is the primary feature of cognitive radios for wireless communication systems Genetic algorithms (GAs) are designed to select the optimal transmission parameters by scoring a subset of parameters and evolving them until the optimal value is reached for a given goal Although there have been implementations of GA-based single carrier cognitive radio engines, the performance of these algorithms has not been thoroughly analyzed nor have the fitness functions employed by the algorithms been explored in detail Multicarrier systems are common in today's communication environment, thus cognitive techniques that account for only single-carrier systems neglect the practical issues of multiple carriers A set of accurate single carrier and multicarrier fitness functions for our GA implementation that completely control the evolution of the algorithm have been derived The performance analysis results illustrate the trade-offs between the convergence time of the GA and the size of the GA search space Copyright © 2007 John Wiley & Sons, Ltd

156 citations

### Cites background from "Neural networks and artificial inte..."

...is neural networks [ 25 ], which tries to solve large, complex problems by analyzing information...

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...network [ 25 ]. Rule-based systems are derivatives of knowledge-based systems, where instead of...

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TL;DR: An alternative approach, based on artificial neural networks with the back-propagation algorithm, to map two different transformations: EMG-->joint angles; and (2) EMG -->joint moments, suggesting that neural networks can provide a successful platform for both biomechanical modeling and simulation.

Abstract: Optimization theory and other mathematical algorithms have traditionally been used to model the relationship between muscle activity and lower-limb dynamics during human gait. We introduce here an alternative approach, based on artificial neural networks with the back-propagation algorithm, to map two different transformations: (1) EMG → joint angles; and (2) EMG → joint moments. Normal data for 16 muscles and three joint moments and angles (hip, knee, and ankle) were adapted from the literature [Winter (1987), The Biomechanics and Motor Control of Human Gait ]. Both networks were successfully trained to map the input vector onto the output vector. The models were tested by feeding in an input vector where all 16 muscles were slightly different (20%) from the training data, and the predicted output vectors suggested that the models were valid. The trained networks were then used to perform two separate simulations: 30% reduction in soleus activity; and removal of rectus femoris. Net 2, in which electromyography was mapped onto joint moments, provided the most reasonable results, suggesting that neural networks can provide a successful platform for both biomechanical modeling and simulation. We believe that this paper has demonstrated the potential of artificial neural networks, and that further efforts should be directed towards the development of larger training sets based on normal and pathological data.

154 citations

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TL;DR: A review of a variety of supervised neural networks with online learning capabilities, focusing on articles published in main indexed journals in the past 10 years, examines a number of key neural network architectures.

Abstract: Learning in neural networks can broadly be divided into two categories, viz., off-line (or batch) learning and online (or incremental) learning. In this paper, a review of a variety of supervised neural networks with online learning capabilities is presented. Specifically, we focus on articles published in main indexed journals in the past 10 years (2003---2013). We examine a number of key neural network architectures, which include feedforward neural networks, recurrent neural networks, fuzzy neural networks, and other related networks. How the online learning methodologies are incorporated into these networks is exemplified, and how they are applied to solving problems in different domains is highlighted. A summary of the review that covers different network architectures and their applications is presented.

94 citations

##### References

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01 Jan 1988

TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.

Abstract: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion

17,604 citations

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TL;DR: A model of a system having a large number of simple equivalent components, based on aspects of neurobiology but readily adapted to integrated circuits, produces a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.

Abstract: Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.

16,652 citations

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TL;DR: In this article, it is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under another and gives the same results, although perhaps not in the same time.

Abstract: Because of the “all-or-none” character of nervous activity, neural events and the relations among them can be treated by means of propositional logic. It is found that the behavior of every net can be described in these terms, with the addition of more complicated logical means for nets containing circles; and that for any logical expression satisfying certain conditions, one can find a net behaving in the fashion it describes. It is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under the other and gives the same results, although perhaps not in the same time. Various applications of the calculus are discussed.

14,937 citations

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03 Jan 1986

TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.

Abstract: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion

13,579 citations

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TL;DR: This article will be concerned primarily with the second and third questions, which are still subject to a vast amount of speculation, and where the few relevant facts currently supplied by neurophysiology have not yet been integrated into an acceptable theory.

Abstract: The first of these questions is in the province of sensory physiology, and is the only one for which appreciable understanding has been achieved. This article will be concerned primarily with the second and third questions, which are still subject to a vast amount of speculation, and where the few relevant facts currently supplied by neurophysiology have not yet been integrated into an acceptable theory. With regard to the second question, two alternative positions have been maintained. The first suggests that storage of sensory information is in the form of coded representations or images, with some sort of one-to-one mapping between the sensory stimulus

8,434 citations