12,340 research outputs found
AMTRAN development program Interim report
AMTRAN software system developed for IBM 1130 compute
Data base management system analysis and performance testing with respect to NASA requirements
Several candidate Data Base Management Systems (DBM's) that could support the NASA End-to-End Data System's Integrated Data Base Management System (IDBMS) Project, later rescoped and renamed the Packet Management System (PMS) were evaluated. The candidate DBMS systems which had to run on the Digital Equipment Corporation VAX 11/780 computer system were ORACLE, SEED and RIM. Oracle and RIM are both based on the relational data base model while SEED employs a CODASYL network approach. A single data base application which managed stratospheric temperature profiles was studied. The primary reasons for using this application were an insufficient volume of available PMS-like data, a mandate to use actual rather than simulated data, and the abundance of available temperature profile data
Second CLIPS Conference Proceedings, volume 1
Topics covered at the 2nd CLIPS Conference held at the Johnson Space Center, September 23-25, 1991 are given. Topics include rule groupings, fault detection using expert systems, decision making using expert systems, knowledge representation, computer aided design and debugging expert systems
An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams
Existing FNNs are mostly developed under a shallow network configuration
having lower generalization power than those of deep structures. This paper
proposes a novel self-organizing deep FNN, namely DEVFNN. Fuzzy rules can be
automatically extracted from data streams or removed if they play limited role
during their lifespan. The structure of the network can be deepened on demand
by stacking additional layers using a drift detection method which not only
detects the covariate drift, variations of input space, but also accurately
identifies the real drift, dynamic changes of both feature space and target
space. DEVFNN is developed under the stacked generalization principle via the
feature augmentation concept where a recently developed algorithm, namely
gClass, drives the hidden layer. It is equipped by an automatic feature
selection method which controls activation and deactivation of input attributes
to induce varying subsets of input features. A deep network simplification
procedure is put forward using the concept of hidden layer merging to prevent
uncontrollable growth of dimensionality of input space due to the nature of
feature augmentation approach in building a deep network structure. DEVFNN
works in the sample-wise fashion and is compatible for data stream
applications. The efficacy of DEVFNN has been thoroughly evaluated using seven
datasets with non-stationary properties under the prequential test-then-train
protocol. It has been compared with four popular continual learning algorithms
and its shallow counterpart where DEVFNN demonstrates improvement of
classification accuracy. Moreover, it is also shown that the concept drift
detection method is an effective tool to control the depth of network structure
while the hidden layer merging scenario is capable of simplifying the network
complexity of a deep network with negligible compromise of generalization
performance.Comment: This paper has been published in IEEE Transactions on Fuzzy System
Incremental Analysis of Programs
Algorithms used to determine the control and data flow properties of computer programs are generally designed for one-time analysis of an entire new input. Application of such algorithms when the input is only slightly modified results in an inefficient system. In this theses a set of incremental update algorithms are presented for data flow analysis. These algorithms update the solution from a previous analysis to reflect changes in the program. Thus, extensive reanalysis to reflect changes in the program. Thus, extensive reanalysis of programs after each program modification can be avoided. The incremental update algorithms presented for global flow analysis are based on Hecht/Ullman iterative algorithms. Banning\u27s interprocedural data flow analysis algorithms form the basis for the incremental interprocedural algorithms
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