Incremental Adaptive Networks Research Articles

Theoretical Analysis of the Underwater Incremental Adaptive Network Performance Based on the VLC Technology

In this paper, the underwater implementation of the incremental adaptive networks is proposed based on the visible light communication technology. The underwater distance between transmitter and receiver nodes and the salinity and temperature levels of the considered water determines the stochastical properties of the underwater link that is modeled with the Log-normal distribution. The incremental network performance can be expressed with the excess mean square error and mean square deviation values and we used them in this paper for our theoretical analysis. Our findings showed that the distances between the nodes must not be more than 10 m or the incremental network will diverge from its estimation goal. The network performance is analyzed through multiple link distances and the results are presented with several simulations. The simulation results are devised in order to elaborate the effects of the underwater turbulent links on the performances of estimating adaptive network. Also, the impacts of different salinity and temperature levels are analyzed theoretically and the results are compared with the simulation results.

Performance Analysis of Wireless Adaptive Incremental Networks Under Strong FSO Link Turbulence Conditions

The aim of this paper is to show the problems of implementing the wireless adaptive networks with the free space optical (FSO) technology. Implementing adaptive networks with the wireless optical communication technology has several benefits and also some hindering problems. The thermal optical noise modeled with Gaussian distribution and link turbulence is two of the major problems of this implementation. In this paper, the theoretical analysis of the FSO link effects that are modeled with K-distribution and Negative exponential distributions are considered on the estimation performance of the adaptive incremental networks. These distributions arise when the FSO link is contaminated with strong optical turbulence. Experiments are designed to cover these conditions and the analysis is based on the steady state mean square deviation (MSD) and excess mean square error (EMSE) values for the incremental LMS (ILMS) algorithm and these are the metrics that show how well the adaptive network performs. Simulation results are presented for different parameters of $K$ -distribution and negative exponential distribution and the results show perfect match with the theoretical outcomes. Based on these results, we show that implementing the incremental adaptive networks in the strong turbulence conditions is not feasible and we must think of some countermeasures for these cases.

The node distance effects on the performance of the underwater VLC incremental adaptive networks

ABSTRACTIn this paper, an underwater implementation of incremental networks based on visible light communication technology is described and analysed. The underwater distance between transmitter and receiver nodes and the salinity and temperature levels of the water yield stochastic properties of the link that can be modelled with the Log-normal distribution. The incremental network performance can be expressed using excess mean square error and Mean square deviation metrics. Our findings showed that the distance between nodes must not exceed 10 metres; if it does, the network performance will diverge from its estimation goal. The performance is analysed through multiple link distances and results are presented for simulations. The impacts of various salinity and temperature levels are analysed simultaneously.

Distributed acoustic signature identification using incremental adaptive networks

Considerable amount of information about objects is obtained from their acoustic signature. Since the acoustic signal is physically propagated over a geographical region, any fixed point observer records data with a limited quality. Moving sources also decreases the generality of single point modeling. In this paper, we propose employing distributed incremental adaptive networks for the aim of acoustic signature identification, as a time-varying autoregressive (TVAR) stochastic process. The distributed adaptive sensor network considers spatial and temporal challenges simultaneously and provides real-time estimations. By formulating the problem under non-stationary conditions, we proceed to show the superiority of the proposed incremental adaptive algorithm compared to the classical single point observations methods. To practically prove this merit, the proposed algorithms were implemented on and evaluated using a real sensor network dataset recorded from moving vehicles for the first time in the adaptive networks field; this is a substantial real-world validation test. The experimental results well support the claim and demonstrate the excellence and competence of distributed incremental adaptive networks for this case.

An incremental LMS network with reduced communication delay

Successful implementation of incremental adaptive networks requires that the sampling speed of the network be faster than the time between two consecutive measurements taken by nodes. This issue affects the performance of incremental based algorithm in networks with large number of nodes as well as non-stationary environments. In this paper, we introduce a modified incremental least mean-square (ILMS) algorithm that resolves this problem. In the proposed algorithm, every node updates its local estimate when the measurement data are available (temporal processing). When the estimate from the previous node is also available, an adaptive strategy is used to combine the available estimates to further improve the network learning performance (spatial cooperation). We examine the performance of the proposed algorithm in two scenarios including the network with noisy links and non-stationary environment. Simulation results show the superior performance of the proposed algorithm in comparison with the original ILMS algorithm.

Environment and Goals Jointly Direct Category Acquisition

Developing categorization schemes involves discovering structures in the world that support a learner's goals. Existing models of category learning, such as exemplar and prototype models, neglect the role of goals in shaping conceptual organization. Here, a clustering approach is discussed that reflects the joint influences of the environment and goals in directing category acquisition. Clusters are a flexible representational medium that exhibits properties of exemplar, prototype, and rule-based models. Clusters reflect the natural bundles of correlated features present in our environment. The clustering model Supervised and Unsupervised Stratified Incremental Adaptive Network (SUSTAIN) operates by assuming the world has a simple structure and adding complexity (i.e., clusters) when existing clusters fail to satisfy the learner's goals and thus elicit surprise. Although simple, this operation is sufficient to address findings from numerous laboratory and cross-cultural categorization studies.

An Incremental Adaptive Network for On-line Supervised Learning and Probability Estimation

In this paper, a novel hybrid utilization of the Fuzzy ARTMAP (FAM) neural network and the Probabilistic Neural Network (PNN) is proposed for on-line learning and probability estimation tasks. There are two distinct advantages to the hybrid network. First, FAM is used as an underlying clustering algorithm to classify the input patterns into different recognition categories during the learning phase, resulting in a significant reduction in the number of pattern nodes required in the PNN. Second, a non-parametric posterior probability distribution estimation procedure, in accordance with the PNN paradigm (i.e. the Parzen-windows estimator), is employed during the prediction phase, where a probabilistic interpretation corresponding to Bayes decision theory can be provided for the predictions of FAM. In addition, several modifications are proposed to integrate both of the networks effectively into a unified platform for enhancing generalization. This hybrid approach also realizes an incremental learning system in which the necessity to specify a static network configuration a priori is eliminated as the network is able to “grow” to accommodate new input patterns sequentially and can thus operate in non-stationary environments. The performance of the network is evaluated with benchmark classification tasks and the results are compared with other approaches. Simulation results indicate that this hybrid network is capable of achieving a value near to the Bayes optimal classification rate. © 1997 Elsevier Science Ltd.