A SA‐ANN‐Based Modeling Method for Human Cognition Mechanism and the PSACO Cognition Algorithm

Chen, Shuting; Tan, Dapeng

doi:10.1155/2018/6264124

Cited by 24 publications

(11 citation statements)

References 62 publications

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“…The learning takes place when synaptic connections are adjusted and it has the ability to learn on the basis of input data [41]. For addressing complex information understanding in cognition, Artificial Neural Network for human cognition is significant [42]. Greene [43] elaborates the usefulness of various aspects of human cognition and memory structuring is one important element.…”

Section: Related Workmentioning

confidence: 99%

In-Vehicle Cognitive Route Decision Using Fuzzy Modeling and Artificial Neural Network

Saeed

Ahmed²,

Zareei

et al. 2019

IEEE Access

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The departments of transportation worldwide are facing various challenges despite introducing and incorporating various vehicular features. One of such challenges is to make vehicles autonomous, intelligent, and capable of self-learning to evolve their knowledge repository. In this paper, human cognition is proposed to be implemented in vehicles so that they can perform human-like decisions. Therefore, the process of vehicular route decision is debated cognitively in order to provide route information intelligently. The in-vehicle routes provided by the GPS are not optimal and lack on-demand user requirements. GPS connectivity issues, in certain conditions, make it difficult for vehicles to take real-time decisions. This leads to the idea of self-decision by the vehicle controller. We propose a cognitive framework for vehicles to make self-decisions that use cognitive memory for storing route experiences. The framework strengthens the existing in-vehicle route finding capability and its provision in a more realistic manner. The user is provided with all available route-related information that is required for the journey. In addition, the route episodes are learned, stored, and accessed inside the cognitive memory for an optimal route provision. The vehicle learns about the routes and matures with route-experience by itself with the passage of time. In simulations, fuzzy modeling is used to validate the impact of cognitive parameters over static/conventional parameters. Moreover, artificial neural networks are used to minimize the error rate in learning to achieve cognitive route decisions. The proposed in-vehicle cognitive framework outperforms the existing route provision system that is inadequate and provokes the user's anxieties during driving. Besides, the proposed scheme gradually gets mature in delivering optimal as well as latest route-related information. INDEX TERMS VANET, cognition, fuzzy model, artificial neural network, vehicle route.

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Section: Related Workmentioning

confidence: 99%

In-Vehicle Cognitive Route Decision Using Fuzzy Modeling and Artificial Neural Network

Saeed

Ahmed²,

Zareei

et al. 2019

IEEE Access

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“…It is a kind of processing medium with low abrasive particle fraction and high motion velocity, and the abrasive particle fraction is generally less than 15%. Abrasive flow is a typical two-phase incompressible fluid, in which the distilled water is the carrier of abrasive particles, and the electrostatic dispersant can address the even particle distribution of abrasive flow [34][35][36][37][38][39].…”

Section: Multi-segment Profiling Constrained Flow Passagementioning

confidence: 99%

An Improved Abrasive Flow Processing Method for Complex Geometric Surfaces of Titanium Alloy Artificial Joints

et al. 2018

Self Cite

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“…During the RLS-based system parameter identification, the data saturation can influence the identifying accuracy and cause negative effects for the temperature control system [36,37]. To address the matter, an ANN-based forgetting factor searching (ANN-FFS) algorithm is proposed, and the steps are described as follows.…”

Section: Pole-point Assignmentmentioning

confidence: 99%

A Self‐Tuning Proportional‐Integral‐Derivative‐Based Temperature Control Method for Draw‐Texturing‐Yarn Machine

Rong

Chen

2017

Mathematical Problems in Engineering

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Owing to the fast time-varying characteristics, the temperature control for draw-texturing-yarn (DTY) machine has higher technical difficulties and results in challenges for system energy optimization. To address the matter, a self-tuning proportionalintegral-derivative-(ST-PID-) based temperature control method is proposed. Referring to the technical procedures of DTY machine, a thermodynamic model is set up. Then, a ST-PID minimum phase control system is constructed by the pole-point placement method. Subsequently, an artificial neural network based forgetting factor searching (ANN-FFS) algorithm is developed to optimize the system parameter identification. The numerical cases show that the proposed ANN-FFS algorithm can improve the parameter identification process, and the average identifying efficiency ( > 15) can increase by more than 50%; compared with the fuzzy PID controller, the proposed ST-PID method can increase the control accuracy nearly 3 times for the static temperature ascending. The experimental results prove that the proposed ST-PID method has better abilities of characteristics tracing and antiinterference and can restrain the temperature fluctuation caused by objective switching and the factual control accuracy reaches 3 times that of fuzzy PID method.

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A SA‐ANN‐Based Modeling Method for Human Cognition Mechanism and the PSACO Cognition Algorithm

Cited by 24 publications

References 62 publications

In-Vehicle Cognitive Route Decision Using Fuzzy Modeling and Artificial Neural Network

In-Vehicle Cognitive Route Decision Using Fuzzy Modeling and Artificial Neural Network

An Improved Abrasive Flow Processing Method for Complex Geometric Surfaces of Titanium Alloy Artificial Joints

A Self‐Tuning Proportional‐Integral‐Derivative‐Based Temperature Control Method for Draw‐Texturing‐Yarn Machine

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