230 research outputs found
Multiobjective programming for type-2 hierarchical fuzzy inference trees
This paper proposes a design of hierarchical fuzzy inference tree (HFIT). An HFIT produces an
optimum tree-like structure. Specifically, a natural hierarchical structure that accommodates simplicity by
combining several low-dimensional fuzzy inference systems (FISs). Such a natural hierarchical structure
provides a high degree of approximation accuracy. The construction of HFIT takes place in two phases.
Firstly, a nondominated sorting based multiobjective genetic programming (MOGP) is applied to obtain a
simple tree structure (low model’s complexity) with a high accuracy. Secondly, the differential evolution
algorithm is applied to optimize the obtained tree’s parameters. In the obtained tree, each node has a
different input’s combination, where the evolutionary process governs the input’s combination. Hence,
HFIT nodes are heterogeneous in nature, which leads to a high diversity among the rules generated
by the HFIT. Additionally, the HFIT provides an automatic feature selection because it uses MOGP
for the tree’s structural optimization that accept inputs only relevant to the knowledge contained in
data. The HFIT was studied in the context of both type-1 and type-2 FISs, and its performance was
evaluated through six application problems. Moreover, the proposed multiobjective HFIT was compared
both theoretically and empirically with recently proposed FISs methods from the literature, such as
McIT2FIS, TSCIT2FNN, SIT2FNN, RIT2FNS-WB, eT2FIS, MRIT2NFS, IT2FNN-SVR, etc. From the
obtained results, it was found that the HFIT provided less complex and highly accurate models compared
to the models produced by most of the other methods. Hence, the proposed HFIT is an efficient and
competitive alternative to the other FISs for function approximation and feature selectio
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Advances in Evolutionary Algorithms
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New Fundamental Technologies in Data Mining
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Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences
Mathematical fuzzy logic (MFL) specifically targets many-valued logic and has significantly contributed to the logical foundations of fuzzy set theory (FST). It explores the computational and philosophical rationale behind the uncertainty due to imprecision in the backdrop of traditional mathematical logic. Since uncertainty is present in almost every real-world application, it is essential to develop novel approaches and tools for efficient processing. This book is the collection of the publications in the Special Issue “Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences”, which aims to cover theoretical and practical aspects of MFL and FST. Specifically, this book addresses several problems, such as:- Industrial optimization problems- Multi-criteria decision-making- Financial forecasting problems- Image processing- Educational data mining- Explainable artificial intelligence, etc
Human–machine network through bio‑inspired decentralized swarm intelligence and heterogeneous teaming in SAR operations
Disaster management has always been a struggle due to unpredictable changing conditions and chaotic occurrences that require real-time adaption. Highly optimized missions and robust systems mitigate uncertainty effects and improve notoriously success rates. This paper brings a niching hybrid human–machine system that combines UAVs fast responsiveness with two robust, decentralized, and scalable bio-inspired techniques. Cloud-Sharing Network (CSN) and Pseudo-Central Network (PCN), based on Bacterial and Honeybee behaviors, are presented, and applied to Safe and Rescue (SAR) operations. A post-earthquake scenario is proposed, where a heterogeneous fleet of UAVs cooperates with human rescue teams to detect and locate victims distributed along the map. Monte Carlo simulations are carried out to test both approaches through state-of-the-art metrics. This paper introduces two hybrid and bio-inspired schemes to deal with critical scouting stages, poor communications environments and high uncertainly levels in disaster release operations. Role heterogeneity, path optimization and hive data-sharing structure give PCN an efficient performance as far as task allocation and communications are concerned. Cloud-sharing network gains strength when the allocated agents per victim and square meter is high, allowing fast data transmission. Potential applications of these algorithms are not only comprehended in SAR field, but also in surveillance, geophysical mapping, security and planetary exploration
Capturing Risk in Capital Budgeting
NPS NRP Technical ReportThis proposed research has the goal of proposing novel, reusable, extensible, adaptable, and comprehensive advanced analytical process and Integrated Risk Management to help the (DOD) with risk-based capital budgeting, Monte Carlo risk-simulation, predictive analytics, and stochastic optimization of acquisitions and programs portfolios with multiple competing stakeholders while subject to budgetary, risk, schedule, and strategic constraints. The research covers topics of traditional capital budgeting methodologies used in industry, including the market, cost, and income approaches, and explains how some of these traditional methods can be applied in the DOD by using DOD-centric non-economic, logistic, readiness, capabilities, and requirements variables. Stochastic portfolio optimization with dynamic simulations and investment efficient frontiers will be run for the purposes of selecting the best combination of programs and capabilities is also addressed, as are other alternative methods such as average ranking, risk metrics, lexicographic methods, PROMETHEE, ELECTRE, and others. The results include actionable intelligence developed from an analytically robust case study that senior leadership at the DOD may utilize to make optimal decisions. The main deliverables will be a detailed written research report and presentation brief on the approach of capturing risk and uncertainty in capital budgeting analysis. The report will detail the proposed methodology and applications, as well as a summary case study and examples of how the methodology can be applied.N8 - Integration of Capabilities & ResourcesThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.
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