EVA AI: Blog Post 1: Personal Intro, Motivation, Learning Goals

Relevance: 6/10 This paper is more close to control engineering than to main AI topics. The neural network is used, but not in way like deep learning or reinforcement learning. It is not clear if this work is interesting for many AI researchers. If author can compare with more AI method, it will help. Significance: 8/10 The idea of self-organizing RBF network with spiking neuron mechanism is interesting. I think this is good for control system which is nonlinear and changing. The example of wastewater plant is practical and realistic. It show the method is working. But it is only one example. No test in other systems. Also, the paper not say if the method can be used for more fast or real-time system. Originality: 7/10 Author combine many known idea like RBF, recurrent NN, Lyapunov and spiking to make one model. The growing and pruning of hidden neuron is done with new idea. This part I think is creative. But I not understand why this growing method is better than others. Why us...

This paper presents a novel Nonlinear Model Predictive Control (NMPC) strategy based on a Self-Organizing Recurrent Radial Basis Function (SR-RBF) Neural Network. The key innovation lies in the adaptive architecture—both the structure and parameters of the neural network evolve in real-time using a biologically-inspired spiking-based growing and pruning algorithm. What’s new? A self-organizing recurrent neural model that dynamically adjusts its complexity. A spiking strength criterion mimicking biological neurons to decide when to add or remove neurons. An improved gradient-based optimization method for real-time control decisions under constraints. Formal Lyapunov-based stability proof for the whole control framework. Method: The SR-RBF neural network models nonlinear system dynamics. Network structure is updated in real-time using spike-based activity thresholds. An improved gradient method solves the NMPC optimization problem under constraints. Experiments: The...

What makes a Paper good or bad  Title and Abstract: Strong and clear, effectively presenting the main ideas, methods, and outcomes. Introduction: Comprehensive, providing background, identifying gaps in existing methods, and clearly stating the research questions. Methods: Highly detailed, explaining the SR-RBF network design and NMPC formulation with solid mathematical rigor, suitable for technical readers. Results: Rich with data, using figures and tables to convincingly demonstrate the method’s advantages. Discussion: Reflective and insightful, thoughtfully comparing findings to previous work and highlighting their significance. Conclusion: Focused, summarizing key points and emphasizing readiness for real-world application. References and Appendices: Robust and well-integrated, supporting the paper with foundational and current works. Improvements: The paper could be improved by making the math sections more accessible with visual aids, summarizing results earli...

SMART GOAL My goal is to learn how to differentiate between a well-written academic paper and a poorly written one, including aspects such as writing style, structure, argumentation, use of sources, and clarity of expression. To achieve this goal, I will critically analyze one already selected academic paper related to my Master's thesis topic. I will apply the academic research skills I develop in this course, using critical thinking and research tools. This goal is highly relevant to my Master's thesis work, as I need to conduct extensive research and evaluate the quality of Sources and the selected paper aligns closely with my thesis Topic. - Specific: to differentiate between a well-written academic paper and a poorly written one - Measurable: One Paper, by doing the statet tasks - Achievable: by applying the skills I learned in this Semester - Relevant: Topic and developing Research skills is highly relevant for my Thesis - Time: With in one Semester, all tree weeks. 

Task: an introduction about you your motivation for taking this seminar the rationale for selecting the topic you will analyse what are your specific learning goals to achieve by the end of the semester. My Name is J.Acimovic. I study Energy and Environment at the FHNW, at the Institute of Automation. My motivation for taking this seminar is related to my Master's thesis and the Innosuisse project I am working on with the Institute for Data Science. We are using neural networks for wastewater treatment optimization, demand-side control, and forecasting. That’s also why I selected this topic. My specific learning goals are to learn how to conduct proper research using both critical thinking and helpful tools, and to apply this research to my Master's thesis.