In this paper, the problem of hybrid model predictive control(HMPC) strategy based on fuzzy supervisor for piecewise autoregressive with exogenous input(PWARX) models is addressed. We first represent the nonlinear beh...In this paper, the problem of hybrid model predictive control(HMPC) strategy based on fuzzy supervisor for piecewise autoregressive with exogenous input(PWARX) models is addressed. We first represent the nonlinear behavior of the system with a PWARX model. Then, we transform the obtained PWARX model into a mixed logical dynamic(MLD) model in order to apply the proposed predictive control which is able to stabilize such systems along desired reference trajectories while satisfying operating constraints.Finally, we propose to introduce a fuzzy supervisor allowing the readjustment of the HMPC tuning parameters in order to maintain the desired performance. Simulation and experimental results are presented to illustrate the effectiveness of the proposed approach.展开更多
With the continuous evolution and expanding applications of Large Language Models (LLMs), there has been a noticeable surge in the size of the emerging models. It is not solely the growth in model size, primarily meas...With the continuous evolution and expanding applications of Large Language Models (LLMs), there has been a noticeable surge in the size of the emerging models. It is not solely the growth in model size, primarily measured by the number of parameters, but also the subsequent escalation in computational demands, hardware and software prerequisites for training, all culminating in a substantial financial investment as well. In this paper, we present novel techniques like supervision, parallelization, and scoring functions to get better results out of chains of smaller language models, rather than relying solely on scaling up model size. Firstly, we propose an approach to quantify the performance of a Smaller Language Models (SLM) by introducing a corresponding supervisor model that incrementally corrects the encountered errors. Secondly, we propose an approach to utilize two smaller language models (in a network) performing the same task and retrieving the best relevant output from the two, ensuring peak performance for a specific task. Experimental evaluations establish the quantitative accuracy improvements on financial reasoning and arithmetic calculation tasks from utilizing techniques like supervisor models (in a network of model scenario), threshold scoring and parallel processing over a baseline study.展开更多
文摘In this paper, the problem of hybrid model predictive control(HMPC) strategy based on fuzzy supervisor for piecewise autoregressive with exogenous input(PWARX) models is addressed. We first represent the nonlinear behavior of the system with a PWARX model. Then, we transform the obtained PWARX model into a mixed logical dynamic(MLD) model in order to apply the proposed predictive control which is able to stabilize such systems along desired reference trajectories while satisfying operating constraints.Finally, we propose to introduce a fuzzy supervisor allowing the readjustment of the HMPC tuning parameters in order to maintain the desired performance. Simulation and experimental results are presented to illustrate the effectiveness of the proposed approach.
文摘With the continuous evolution and expanding applications of Large Language Models (LLMs), there has been a noticeable surge in the size of the emerging models. It is not solely the growth in model size, primarily measured by the number of parameters, but also the subsequent escalation in computational demands, hardware and software prerequisites for training, all culminating in a substantial financial investment as well. In this paper, we present novel techniques like supervision, parallelization, and scoring functions to get better results out of chains of smaller language models, rather than relying solely on scaling up model size. Firstly, we propose an approach to quantify the performance of a Smaller Language Models (SLM) by introducing a corresponding supervisor model that incrementally corrects the encountered errors. Secondly, we propose an approach to utilize two smaller language models (in a network) performing the same task and retrieving the best relevant output from the two, ensuring peak performance for a specific task. Experimental evaluations establish the quantitative accuracy improvements on financial reasoning and arithmetic calculation tasks from utilizing techniques like supervisor models (in a network of model scenario), threshold scoring and parallel processing over a baseline study.
