9,322 research outputs found
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
Design of an Adaptive Neurofuzzy Inference Control System for the Unified Power-Flow Controller
This paper presents a new approach to control the operation of the unified power-flow controller (UPFC) based on the adaptive neurofuzzy inference controller (ANFIC) concept. The training data for the controller are extracted from an analytical model of the transmission system incorporating a UPFC. The operating points' space is dynamically partitioned into two regions: 1) an inner region where the desired operating point can be achieved without violating any of the UPFC constraints and 2) an outer region where it is necessary to operate the UPFC beyond its limits. The controller is designed to achieve the most appropriate operating point based on the real power priority. In this study, the authors investigated and analyzed the effect of the system short-circuit level on the UPFC operating feasible region which defines the limitation of its parameters. In order to illustrate the effectiveness of the control algorithm, simulation and experimental studies have been conducted using the MATLAB/SIMULINK and dSPACE DS1103 data-acquisition board. The obtained results show a clear agreement between simulation and experimental results which verify the effective performance of the ANFIC controller
Fuzzy-logic framework for future dynamic cellular systems
There is a growing need to develop more robust and energy-efficient network architectures to cope with ever increasing traffic and energy demands. The aim is also to achieve energy-efficient adaptive cellular system architecture capable of delivering a high quality of service (QoS) whilst optimising energy consumption. To gain significant energy savings, new dynamic architectures will allow future systems to achieve energy saving whilst maintaining QoS at different levels of traffic demand. We consider a heterogeneous cellular system where the elements of it can adapt and change their architecture depending on the network demand. We demonstrate substantial savings of energy, especially in low-traffic periods where most mobile systems are over engineered. Energy savings are also achieved in high-traffic periods by capitalising on traffic variations in the spatial domain. We adopt a fuzzy-logic algorithm for the multi-objective decisions we face in the system, where it provides stability and the ability to handle imprecise data
1.5V fully programmable CMOS Membership Function Generator Circuit with proportional DC-voltage control
A Membership Function Generator Circuit (MFGC) with bias supply of 1.5 Volts and independent DC-voltage programmable functionalities is presented. The realization is based on a programmable differential current mirror and three compact voltage-to-current converters, allowing continuous and quasi-linear adjustment of the center position, height, width and slopes of the triangular/trapezoidal output waveforms. HSPICE simulation results of the proposed circuit using the parameters of a double-poly, three metal layers, 0.5 μm CMOS technology validate the functionality of the proposed architecture, which exhibits a maximum deviation of the linearity in the programmability of 7 %
Interactive Simplifier Tracing and Debugging in Isabelle
The Isabelle proof assistant comes equipped with a very powerful tactic for
term simplification. While tremendously useful, the results of simplifying a
term do not always match the user's expectation: sometimes, the resulting term
is not in the form the user expected, or the simplifier fails to apply a rule.
We describe a new, interactive tracing facility which offers insight into the
hierarchical structure of the simplification with user-defined filtering,
memoization and search. The new simplifier trace is integrated into the
Isabelle/jEdit Prover IDE.Comment: Conferences on Intelligent Computer Mathematics, 201
A recent electronic control circuit to a throttle device
The main objective of this paper is to conceive a recent electronic control circuit to the throttle device. The throttle mechanical actuator is the most important part in an automotive gasoline engine. Among the different control strategies recently reported, an easy to implement control scheme is an open research topic in the analog electronic engineering field. Hence, by using the nonlinear dwell switching control theory, an analog electronic control unit is proposed to manipulate an automotive throttle plate. Due to the switching mechanism is commuting between a stable and an unstable controllers, the resultant closed-loop system is enough robust to the control objective
This fact is experimentally evidenced. The proposed electronic controller uses operational amplifiers
along with an Arduino unit. This unit is just employed to generate the related switching signal that
can be replaced by using, for instance, the timer IC555. Thus, this study is a contribution on design
and realization of an electronic control circuit to the throttle device.Peer ReviewedPostprint (published version
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