Optimal Control using Evolutionary Algorithms through Neural network based TRANSFORMation

Conventional direct methods of solving Optimal Control (OC) problems lead to large scale optimization formulations, making the classical optimization solvers more preferable over evolutionary optimization algorithms, for solving the single and multiple objective formulations in OC. On the other hand, population based evolutionary optimization solvers have the ability to identify the global basin efficiently. Therefore, in this paper, a novel method termed as TRANSFORM Artificial Neural Network (ANN) assisted reformulation of OC, has been proposed, which transforms the large scale optimization problem into weight training exercise of auto-tuned ANNs that in turn reduces the scale of optimization by several folds. Through this reformulation, the implementation of evolutionary optimization algorithm is enabled for solving both single and multiple objective OC formulations. Three different benchmark case studies are considered from literature to test the efficiency of proposed algorithm -(a) control of a batch reactor for maximizing the yield of penicillin production, (b) optimal drug scheduling for maximizing the success rates in chemotherapy for cancer treatment, and (c) multi-objective control of plug flow reactor with energy and conversion trade-off. Results indicated an average 50-fold reduction in OC problem size due to ANN reformulation

A Deep Unsupervised Learning Algorithm for Dynamic Data Clustering

Owing to the generation of vast amount of unlabelled dynamic data and the need to analyze them, deep unsupervised learning based clustering algorithms are gaining importance in the field of data science. Since the task of automated feature extraction is proficiently combined with the machine learning models in deep unsupervised learning algorithms, they are identified to be superior as compared to conventional dynamic similarity measure based clustering methods. In this context, the authors present a recurrent neural network (RNN) based clustering algorithm optimization, where the vital information representing the dynamic data (or time-series data) is extracted first and subsequently clustered using a soft clustering algorithm. This methodology not only ensures dynamic component extraction in terms of static features but also clusters them efficiently using an evolutionary clustering algorithm called Neuro-Fuzzy C-Means (NFCM) clustering, which reduces the large-scale optimization problem of FCM to small-scale along-with identification of optimal number of clusters. The proposed algorithm has been implemented on three different test data sets collected from machine learning repository and it was found that the results are 98-100% accurate. © 2021 IEEE

Synchronicity Identification in Hippocampal Neurons using Artificial Neural Network assisted Fuzzy C-means Clustering

Neural synchronicity plays a vital role in monitoring the functions that are cognitive. Any disturbance identified in the neural synchrony might lead to a diseased state. In the case of in vitro cell recordings, the neurons demonstrate significant heterogeneity in the firing pattern. Thus, the task of automated identification of synchronous and asynchronous neurons from a large population of neuronal cells remains challenging. To address this issue, an efficient unsupervised machine learning approach has been proposed for a system of primary cultures of hippocampal neurons. Here, a confocal microscope is used for imaging of intracellular calcium using Fluo-4 as the fluorescent indicator. The obtained static images are transformed into time-varying data of cytosolic calcium. Subsequently, an intelligent artificial neural network (ANN) assisted fuzzy clustering algorithm is proposed for grouping the synchronous neurons from a heterogeneous set of calcium data that are spiking in nature. This novel algorithm enables a drastic variable reduction followed by the implementation of a global optimization algorithm to solve the problem in Fuzzy C-means (FCM) clustering. Additionally, the proposed technique computes the optimal cluster number and the hyper-parameters involved in ANNs. To validate the result obtained from ANN assisted FCM, a correlation coefficient, and a spiking pattern plot is analyzed for both the synchronous and asynchronous neuronal cells. Besides this, the proposed algorithm is compared with the traditional FCM, where the solution quality is found to be improved along-with an 88% reduction in decision variable count. The complete novel framework combines the aspects of calcium imaging, ANN-assisted FCM, validation, and comparison, which as a whole, can be used for quick and effective quantification of synchronicity