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

99
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 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....
A Distributed Fault-Tolerant Topology Control Algorithm for Heterogeneous Wireless Sensor Networks Bagci, Hakki; KÖRPEOĞLU, İBRAHİM; Yazıcı, Adnan (Institute of Electrical and Electronics Engineers (IEEE), 2015-04-01) This paper introduces a distributed fault-tolerant topology control algorithm, called the Disjoint Path Vector (DPV), for heterogeneous wireless sensor networks composed of a large number of sensor nodes with limited energy and computing capability and several supernodes with unlimited energy resources. The DPV algorithm addresses the k-degree Anycast Topology Control problem where the main objective is to assign each sensor's transmission range such that each has at least k-vertex-disjoint paths to superno...
Computational representation of protein sequences for homology detection and classification Oğul, Hasan; Mumcuoğlu, Ünal Erkan; Department of Information Systems (2006) Machine learning techniques have been widely used for classification problems in computational biology. They require that the input must be a collection of fixedlength feature vectors. Since proteins are of varying lengths, there is a need for a means of representing protein sequences by a fixed-number of features. This thesis introduces three novel methods for this purpose: n-peptide compositions with reduced alphabets, pairwise similarity scores by maximal unique matches, and pairwise similarity scores by...

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.