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The Levenberg-Marquardt (LM) optimization algorithm has been widely used forsolving machine learning problems. Literature reviews have shown that the LMcan be very powerful and effective on moderate function approximation problemswhen the number of weights in the network is not more than a couple of hundred.In contrast, the LM does not seem to perform as well when dealing with patternrecognition or classification problems, and inefficient when networks becomelarge (e.g. with more than 500 weights). In this paper, we exploit the truepower of LM algorithm using some real world aircraft datasets. On thesedatasets most other commonly used optimizers are unable to detect the anomaliescaused by the changing conditions of the aircraft engine. The challengingnature of the datasets are the abrupt changes in the time series data. We findthat the LM optimizer has a much better ability to approximate abrupt changesand detect anomalies than other optimizers. We compare the performance, inaddressing this anomaly/change detection problem, of the LM and several otheroptimizers. We assess the relative performance based on a range of measuresincluding network complexity (i.e. number of weights), fitting accuracy, overfitting, training time, use of GPUs and memory requirement etc. We also discussthe issue of robust LM implementation in MATLAB and Tensorflow for promotingmore popular usage of the LM algorithm and potential use of LM optimizer forlarge-scale problems.

Exploiting the Power of Levenberg-Marquardt Optimizer with Anomaly Detection in Time Series