112,466 research outputs found
Identification of cellular automata based on incomplete observations with bounded time gaps
In this paper, the problem of identifying the cellular automata (CAs) is considered. We frame and solve this problem in the context of incomplete observations, i.e., prerecorded, incomplete configurations of the system at certain, and unknown time stamps. We consider 1-D, deterministic, two-state CAs only. An identification method based on a genetic algorithm with individuals of variable length is proposed. The experimental results show that the proposed method is highly effective. In addition, connections between the dynamical properties of CAs (Lyapunov exponents and behavioral classes) and the performance of the identification algorithm are established and analyzed
Delay Learning Architectures for Memory and Classification
We present a neuromorphic spiking neural network, the DELTRON, that can
remember and store patterns by changing the delays of every connection as
opposed to modifying the weights. The advantage of this architecture over
traditional weight based ones is simpler hardware implementation without
multipliers or digital-analog converters (DACs) as well as being suited to
time-based computing. The name is derived due to similarity in the learning
rule with an earlier architecture called Tempotron. The DELTRON can remember
more patterns than other delay-based networks by modifying a few delays to
remember the most 'salient' or synchronous part of every spike pattern. We
present simulations of memory capacity and classification ability of the
DELTRON for different random spatio-temporal spike patterns. The memory
capacity for noisy spike patterns and missing spikes are also shown. Finally,
we present SPICE simulation results of the core circuits involved in a
reconfigurable mixed signal implementation of this architecture.Comment: 27 pages, 20 figure
Rough sets theory for travel demand analysis in Malaysia
This study integrates the rough sets theory into tourism demand analysis. Originated from the area of Artificial Intelligence, the rough sets theory was introduced to disclose important structures and to classify objects. The Rough Sets methodology provides definitions and methods for finding which attributes separates one class or classification from another. Based on this theory can propose a formal framework for the automated transformation of data into knowledge. This makes the rough sets approach a useful classification and pattern recognition technique. This study introduces a new rough sets approach for deriving rules from information table of tourist in Malaysia. The induced rules were able to forecast change in demand with certain accuracy
Data Imputation through the Identification of Local Anomalies
We introduce a comprehensive and statistical framework in a model free
setting for a complete treatment of localized data corruptions due to severe
noise sources, e.g., an occluder in the case of a visual recording. Within this
framework, we propose i) a novel algorithm to efficiently separate, i.e.,
detect and localize, possible corruptions from a given suspicious data instance
and ii) a Maximum A Posteriori (MAP) estimator to impute the corrupted data. As
a generalization to Euclidean distance, we also propose a novel distance
measure, which is based on the ranked deviations among the data attributes and
empirically shown to be superior in separating the corruptions. Our algorithm
first splits the suspicious instance into parts through a binary partitioning
tree in the space of data attributes and iteratively tests those parts to
detect local anomalies using the nominal statistics extracted from an
uncorrupted (clean) reference data set. Once each part is labeled as anomalous
vs normal, the corresponding binary patterns over this tree that characterize
corruptions are identified and the affected attributes are imputed. Under a
certain conditional independency structure assumed for the binary patterns, we
analytically show that the false alarm rate of the introduced algorithm in
detecting the corruptions is independent of the data and can be directly set
without any parameter tuning. The proposed framework is tested over several
well-known machine learning data sets with synthetically generated corruptions;
and experimentally shown to produce remarkable improvements in terms of
classification purposes with strong corruption separation capabilities. Our
experiments also indicate that the proposed algorithms outperform the typical
approaches and are robust to varying training phase conditions
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