Learning Efficiency of Consciousness System for Robot Using Artificial Neural Network

This paper presents learning efficiency of a consciousness system for robot using artificial neural network. The proposed conscious system consists of reason system, feeling system and association system. The three systems are modeled using Module of Nerves for Advanced Dynamics (ModNAD). Artificial neural network of the type of supervised learning with the back propagation is used to train the ModNAD. The reason system imitates behaviour and represents self-condition and other-condition. The feeling system represents sensation and emotion. The association system represents behaviour of self and determines whether self is comfortable or not. A robot is asked to perform cognition and tasks using the consciousness system. Learning converges to about 0.01 within about 900 orders for imitation, pain, solitude and the association modules. It converges to about 0.01 within about 400 orders for the comfort and discomfort modules. It can be concluded that learning in the ModNAD completed after a relatively small number of times because the learning efficiency of the ModNAD artificial neural network is good. The results also show that each ModNAD has a function to imitate and cognize emotion. The consciousness system presented in this paper may be considered as a fundamental step for developing a robot having consciousness and feelings similar to humans

Artificial neural network method in solving smart cities construction fuzzy multi-objective linear programming problems

Nowadays, the increasing demands of smart cities require innovative technologies to optimize resource utilization and minimize costs. This research focuses on the construction sector, specifically the production of advanced high-strength steel. We address practical application problems from the iron and steel factory in Annaba, Algeria, known as fuzzy multi-objective linear programming problems. Two methods are applied: a mathematical model and Artificial Neural Networks (ANNs). Results indicate that the ANN approach offers significant time and effort savings

The nicotinic α6 subunit gene determines variability in chronic pain sensitivity via cross-inhibition of P2X2/3 receptors

International audienceChronic pain is a highly prevalent and poorly managed human health problem. We used microarray-based expression genomics in 25 inbred mouse strains to identify dorsal root ganglion (DRG)-expressed genetic contributors to mechanical allodynia, a prominent symptom of chronic pain. We identified expression levels of Chrna6, which encodes the α6 subunit of the nicotinic acetylcholine receptor (nAChR), as highly associated with allodynia. We confirmed the importance of α6* (α6-containing) nAChRs by analyzing both gain- and loss-of-function mutants. We find that mechanical allodynia associated with neuropathic and inflammatory injuries is significantly altered in α6* mutants, and that α6* but not α4* nicotinic receptors are absolutely required for peripheral and/or spinal nicotine analgesia. Furthermore, we show that Chrna6's role in analgesia is at least partially due to direct interaction and cross-inhibition of α6* nAChRs with P2X2/3 receptors in DRG nociceptors. Finally, we establish the relevance of our results to humans by the observation of genetic association in patients suffering from chronic postsurgical and temporomandibular pain