A temporal neural network model for sequence learning

Download
090671.pdf
1999
Aydemir, Bora
Show full item record
145
views
0
downloads
https://hdl.handle.net/11511/2732
Graduate School of Natural and Applied Sciences, Thesis

Suggestions

A temporal neural network model for constructing connectionist expert system knowledge bases
Alpaslan, Ferda Nur (Elsevier BV, 1996-04-01)
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.
A Sparse Temporal Mesh Model for Brain Decoding
Afrasiyabi, Arman; Onal, Itir; Yarman Vural, Fatoş Tunay (2016-08-23)
One of the major drawbacks of brain decoding from the functional magnetic resonance images (fMRI) is the very high dimension of feature space which consists of thousands of voxels in sequence of brain volumes, recorded during a cognitive stimulus. In this study, we propose a new architecture, called Sparse Temporal Mesh Model (STMM), which reduces the dimension of the feature space by combining the voxel selection methods with the mesh learning method. We, first, select the "most discriminative" voxels usin...
A temporal neurofuzzy model for rule-based systems
Alpaslan, Ferda Nur; Jain, L (1997-05-23)
This paper reports the development of a temporal neuro-fuzzy model using fuzzy reasoning which is capable of representing the temporal information. The system is implemented as a feedforward multilayer neural network. The learning algorithm is a modification of the backpropagation algorithm. The system is aimed to be used in medical diagnosis systems.
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...
A strength-biased prediction model for forecasting exchange rates using support vector machines and genetic algorithms
Ozorhan, Mustafa Onur; Toroslu, İsmail Hakkı; Şehitoğlu, Onur Tolga (2017-11-01)
This paper addresses problem of predicting direction and magnitude of movement of currency pairs in the foreign exchange market. The study uses Support Vector Machine with a novel approach for input data and trading strategy. The input data contain technical indicators generated from currency price data (i.e., open, high, low and close prices) and representation of these technical indicators as trend deterministic signals. The input data are also dynamically adapted to each trading day with genetic algorith...
Citation Formats
B. Aydemir, “A temporal neural network model for sequence learning,” Middle East Technical University, 1999.
METU IIS - Integrated Information Service open@metu.edu.tr