short-term prediction of traffic flow is central to alleviating congestion and controlling the negative impacts of environmental pollution resulting from vehicle emissions on both inter- and intra-urban highways. the strong need to monitor and control congestion time and costs for metropolis in developing countries has therefore motivated the current study. this paper establishes the applicatio...

Recent years have seen a huge increase in the study of drones. There is lot published articles regarding drone, focusing on control optimization, fault detection, safety mechanisms, etc. In most studies focused effects faulty propellers and rotors, there very limited academic research drone arms. this paper, detection based vibration multirotor arms using artificial intelligence (AI) proposed. ...

In emerging heterogeneous networks, seamless vertical handover is a critical issue. There must be trade-off between the decision delay and accuracy. This paper’s concern to contribute reliable making that makes complexity effectiveness. So, paper proposes neuro-fuzzy architecture joints capacity of learning artificial neural networks with power linguistic interpretation fuzzy logic. The can lea...

Parkinson’s disease (PD) is a progressive neurodegenerative disorder. Autism spectrum disorder (ASD) neurodevelopment Clinical decision-making process complex. Due to complex nature of sign and its symptoms clinical decision making may lead misclassification. To deal with such medical problems methods or approaches soft computing play an important role. This paper will focus on presenting integ...

In this study, the road traffic congestion of Dehradun city is evaluated from traffic flow information using fuzzy techniques. Three different approaches namely Sugeno, Mamdani models which are manually tuned techniques, and an Adaptive Neuo-Fuzzy Inference System (ANFIS) which an automated model decides the ranges and parameters of the membership functions using grid partition technique, based...

This paper proposes the application of Adaptive Neuro-Fuzzy Inference System to improve the diagnosis accuracy of dissolved gas analysis for classification of different types of incipient faults in oil immersed power transformers. The proposed method integrated the capabilities of neural network to the robustness of fuzzy logic system for the effectiveness to be embedded in the network structur...

Aircraft burst fault is uncertainty and ambiguity. Considering QAR data as the research object, the fault diagnosis system based on the T-S fuzzy neural network combined with aircraft maintenance processes is built. First, the system designs the network performance oversight function to improve genetic neural network program. Then the fuzzy logic is used to deal with fuzzy rules, which can dete...

-This paper presents a new and simple fuzzy neural network based on structured fuzzy logic processors. The modifications of the referential fuzzy logic processors provide the equality index. The major advantage of the proposed network is that the learning process is faster and it has the capability of self -determining the relative cardinality. The fast learning is due to incorporating the rece...

The present work shows how communication systems benefit from fuzzy logic. From signal processing applications, which process bits at the physical layer in order to face complicate problems of non-Gaussian noise, to practical and robust implementations of these systems and up to higher layers in the communication chain, which are engaged in the protocol design. The ability for modeling uncertai...

This paper presents an adaptive fuzzy computedtroque controller, that enhances fuzzy controllers, with an embedded adjustable two-stages credit assignment and selflearning capability, for uncertain robots, to on-line track a prescribed trajectory. An adaptation law for the parameters of controller is combined with the dead-zone technique Lo guarantee a given attenuationregion of trackingerror i...