A temporal neural network model for constructing connectionist expert system knowledge bases

Date

1996-04-01

Author

Alpaslan, Ferda Nur

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

325
views

0
downloads

This paper introduces a temporal feedforward neural network model that can be applied to a number of neural network application areas, including connectionist expert systems. The neural network model has a multi-layer structure, i.e. the number of layers is not limited. Also, the model has the flexibility of defining output nodes in any layer. This is especially important for connectionist expert system applications.

Subject Keywords

Computer Networks and Communications, Hardware and Architecture, Computer Science Applications

URI

https://hdl.handle.net/11511/48652

Journal

JOURNAL OF NETWORK AND COMPUTER APPLICATIONS

DOI

https://doi.org/10.1006/jnca.1996.0010

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

An FPGA implementation of real-time electro-optic & IR image fusion Çölova, İbrahim Melih; Akar, Gözde; Department of Electrical and Electronics Engineering (2010) In this thesis, a modified 2D Discrete Cosine Transform based electro-optic and IR image fusion algorithm is proposed and implemented on an FPGA platform. The platform is a custom FPGA board which uses ALTERA Stratix III family FPGA. The algorithm is also compared with state of the art image fusion algorithms by means of an image fusion software application GUI developed in Matlab®. The proposed algorithm principally takes corresponding 4x4 pixel blocks of two images to be fused and transforms them by means...
A Cascadable Random Neural Network Chip with Reconfigurable Topology Badaroglu, Mustafa; Halıcı, Uğur; Aybay, Isik; Cerkez, Cuneyt (Oxford University Press (OUP), 2010-03-01) A digital integrated circuit (IC) is realized using the random neural network (RNN) model introduced by Gelenbe. The RNN IC employs both configurable routing and random signaling. In this paper we present the networking/routing aspects as well as the performance results of an RNN network implemented by the RNN IC. In the RNN model, each neuron accumulates arriving signals and can fire if its potential at a given instant of time is strictly positive. Firing occurs at random, the intervals between successive ...
A Cognitive Routing Protocol for Bio-Inspired Networking in the Internet of Nano-Things (IoNT) Al-Turjman, Fadi (Springer Science and Business Media LLC, 2020-10-01) In this paper, we propose a framework for data delivery in nano-scale networks, where numerous wireless sensors are distributed on a human body, small object, tiny plant root, etc. Our framework caters for green energy-efficient applications in the Internet of Nano Things (IoNT) where data is relayed via nano-routers from a multifarious nanonodes towards a gateway connected to a large-scale network such as the Internet. We consider the entire network energy while choosing the next hop for our routed packets...
A systematic approach to the integration of overlapping partitions in service-oriented data grids Sunercan, H. Kevser; Alpdemir, M. Nedim; Çiçekli, Fehime Nihan (Elsevier BV, 2011-06-01) This paper aims to provide a service-oriented data integration solution over data Grids for cases where distributed data sources are partitioned with overlapping sections of various proportions. This is an interesting variation which combines both replicated and partitioned data within the same data management framework. Thus, the data management infrastructure has to deal with specific challenges regarding the identification, access and aggregation of partitioned data with varying proportions of overlappin...
A new likelihood approach to autonomous multiple model estimation Söken, Halil Ersin (Elsevier BV, 2020-04-01) This paper presents an autonomous multiple model (AMM) estimation algorithm for hybrid systems with sudden changes in their parameters. Estimates of Kalman filters (KFs) that are tuned and employed for different system modes are merged based on a newly defined likelihood function without any necessity for filter interaction. The proposed likelihood function is composed of two measures, the filter agility measure and the steady-state error measure. These measures are derived based on filter adaptation rules....

Citation Formats

F. N. Alpaslan, “A temporal neural network model for constructing connectionist expert system knowledge bases,” JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, pp. 119–133, 1996, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/48652.