Introduction to Artificial Intelligence

Ertel, Wolfgang

doi:10.1007/978-3-319-58487-4

Cited by 182 publications

(91 citation statements)

References 0 publications

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“…5(a) is a = (5,6,7,6,7,11,12,13) and that in Fig. 5(b) is a = (7,6,7,6,5,11,13,12). Each wiring activates either element of the X-cells.…”

Section: Digital Spiking Neuronsmentioning

confidence: 97%

“…5(a) gives the characteristic vector of Dmap in Fig. 1: 7,6,7,6,5,11,13,12) The Dmap generates stable PEO and the DSN generates a stable PST as shown in Fig. 6(a).…”

Section: Digital Spiking Neuronsmentioning

confidence: 99%

“…Basically, the evolutionary algorithms use individuals which are potential solutions of an optimization problem for a cost function [10][11][12][13]. The individuals refer to their past history, communicate to each other, and try to find a solution.…”

Section: Introductionmentioning

confidence: 99%

“…As is well known, various evolutionary algorithms have been presented and have been applied to optimization problems in various systems. For example, analog/digital filters, switching power converters, and neural networks [11][12][13][14][15][16][17][18][19]. The design of dynamic digital circuit is an important/effective object in the applications.…”

Section: Introductionmentioning

confidence: 99%

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Realization of desired digital spike-trains by a simple evolutionary algorithm

Saito

Yamaoka

Hamaguchi

2017

NOLTA

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This paper studies realization of desired digital spike-trains based on a simple evolutionary algorithm. First, the dynamics of spike-trains is visualized by a digital spike map. The map is defined on a set of points and is represented by a characteristic vector of integers. Second, in order to realize desired spike-trains, we present a simple evolutionary algorithm that aims at optimization of the characteristic vector for a cost function. We also present a simple method to super-stabilize desired spike-trains. Third, in order to implement the digital spike map, we introduce a digital spiking neuron consisting of two shift registers and a wiring between them. An elementary FPGA-based circuit is presented and a super-stable spike-train is demonstrated.

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“…5(a) is a = (5,6,7,6,7,11,12,13) and that in Fig. 5(b) is a = (7,6,7,6,5,11,13,12). Each wiring activates either element of the X-cells.…”

Section: Digital Spiking Neuronsmentioning

confidence: 97%

“…5(a) gives the characteristic vector of Dmap in Fig. 1: 7,6,7,6,5,11,13,12) The Dmap generates stable PEO and the DSN generates a stable PST as shown in Fig. 6(a).…”

Section: Digital Spiking Neuronsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Realization of desired digital spike-trains by a simple evolutionary algorithm

Saito

Yamaoka

Hamaguchi

2017

NOLTA

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“…It is particularly useful for problems that do not have an an-alytical formulation or where explicit and accessible knowledge does not exist. In the present work, the ANN consists in a fully connected multilayer perceptron (Ertel, 2009), with one hidden layer with H processing units, bias connections, and logistic activation functions 1/(1 + e (−x) ). To find the best value for H, a grid search within the range {2, 4, .…”

Section: Modelling Evaluation and Predictionmentioning

confidence: 99%

Modelling Tyre-Road Noise with Data Mining Techniques

Freitas¹,

Tinoco²,

Soares³

et al. 2015

Archives of Acoustics

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The research aimed to establish tyre-road noise models by using a Data Mining approach that allowed to build a predictive model and assess the importance of the tested input variables. The data modelling took into account three learning algorithms and three metrics to define the best predictive model. The variables tested included basic properties of pavement surfaces, macrotexture, megatexture, and unevenness and, for the first time, damping. Also, the importance of those variables was measured by using a sensitivity analysis procedure. Two types of models were set: one with basic variables and another with complex variables, such as megatexture and damping, all as a function of vehicles speed. More detailed models were additionally set by the speed level. As a result, several models with very good tyre-road noise predictive capacity were achieved. The most relevant variables were Speed, Temperature, Aggregate size, Mean Profile Depth, and Damping, which had the highest importance, even though influenced by speed. Megatexture and IRI had the lowest importance. The applicability of the models developed in this work is relevant for trucks tyre-noise prediction, represented by the AVON V4 test tyre, at the early stage of road pavements use. Therefore, the obtained models are highly useful for the design of pavements and for noise prediction by road authorities and contractors.

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