In this paper, a fuzzy-neural adaptive iterative learning control (AILC) is proposed for traffic flow systems of a single lane freeway with random bounded off-ramp traffic volumes. It is assumed that the system dynamic functions and input gains are unknown for controller design. An adaptive fuzzy neural network (FNN) controller and an adaptive robust controller are applied to compensate for the...

With the rapid development of natural gas pipeline, the safety of natural gas pipeline is paid more and more attention to. This paper presents a new safety assessment model based on fault tree and fuzzy neural network. Firstly, in the light of fault tree theory, fault tree analytical figure is established for buried natural gas pipeline system and the index system is established about buried na...

| This paper introduces hardware implementations of Arti cial Neural Networks and Fuzzy Systems. Several implementation methodologies are described, ranging from fully digital to fully analog ones. Advantages and drawbacks are outlined and performance of existing implementations are presented. The paper also analyzes hardware performance parameters and tradeo s, and the bottlenecks intrinsic in...

It has been one of the great challenges of neuro-symbolic integration to represent recursive logic programs using neural networks of finite size. In this paper, we propose to implement neural networks that can process recursive programs viewed as inductive definitions.

In this paper, we demonstrate the usage of connectionist networks for intelligent prediction of power factor and efficient utilization of power. We propose two neuro-fuzzy networks and artificial neural networks using various learning techniques. The proposed connectionist networks are trained with the plant load current and a highly fluctuated voltage for online prediction of power factor. For...

background and aims: depression disorder is one of the most common diseases, but the diagnosis is widely complicated and controversial because of interventions, overlapping and confusing nature of the disease. so, keeping previous patients’ profile seems effective for diagnosis and treatment of present patients. use of this memory is latent in synthetic neuro-fuzzy algorithm. present article in...

The ability of Multi-Layer Perceptron (MLP) and Neuro-Fuzzy neural networks to classify corn seed varieties based on mixed morphological and color Features has been evaluated that would be helpful for automation of corn handling. This research was done in Islamic Azad University, Shahr-e-Rey Branch, during 2011 on 5 main corn varieties were grown in different environments of Iran. A total of 12...

Abstract: In this paper we present the Beta function and its main properties. A key feature of the Beta function, which is given by the central-limit theorem, is also shown. We then introduce a new category of neural networks based on a new kernel: the Beta function. Next, we investigate the use of Beta fuzzy basis functions for the design of fuzzy logic systems. The functional equivalence betw...

A comparative study on a short-term water level prediction using arti/cial neural networks (ANN) and neuro-fuzzy system is addressed in this paper. The performance of the traditional approaches applied for such a hydrological task can often be constrained by data availability and simplifying assumptions in the processes description. In this paper, the ANN and neuro-fuzzy approaches are used for...

Accurate prediction of solar activity as one aspect of space weather phenomena is essential to decrease the damage from these activities on the ground based communication, power grids, etc. Recently, the connectionist models of the brain such as neural networks and neuro-fuzzy methods have been proposed to forecast space weather phenomena; however, they have not been able to predict solar activ...