A particle swarm optimisation-based Grey prediction model for thermal error compensation on CNC machine tools

Thermal errors can have a significant effect on CNC machine tool accuracy. The thermal error compensation system has become a cost-effective method of improving machine tool accuracy in recent years. In the presented paper, the Grey relational analysis (GRA) was employed to obtain the similarity degrees between fixed temperature sensors and the thermal response of the CNC machine tool structure. Subsequently, a new Grey model with convolution integral GMC(1, N) is used to design a thermal prediction model. To improve the accuracy of the proposed model, the generation coefficients of GMC(1, N) are calibrated using an adaptive Particle Swarm Optimisation (PSO) algorithm. The results demonstrate good agreement between the experimental and predicted thermal error. Finally, the capabilities and the limitations of the model for thermal error compensation have been discussed. Keywords: CNC machine tool, Thermal error modelling, ANFIS, Fuzzy logic, Grey system theory

Contextualized property market models vs. Generalized mass appraisals: An innovative approach

The present research takes into account the current and widespread need for rational valuation methodologies, able to correctly interpret the available market data. An innovative automated valuation model has been simultaneously implemented to three Italian study samples, each one constituted by two-hundred residential units sold in the years 2016-2017. The ability to generate a "unique" functional form for the three different territorial contexts considered, in which the relationships between the influencing factors and the selling prices are specified by different multiplicative coefficients that appropriately represent the market phenomena of each case study analyzed, is the main contribution of the proposed methodology. The method can provide support for private operators in the assessment of the territorial investment conveniences and for the public entities in the decisional phases regarding future tax and urban planning policies

Hybrid Models Of Fuzzy Artmap And Qlearning For Pattern Classification

Pengelasan corak adalah salah satu isu utama dalam pelbagai tugas pencarian data. Dalam kajian ini, fokus penyelidikan tertumpu kepada reka bentuk dan pembinaan model hibrid yang menggabungkan rangkaian neural Teori Resonan Adaptif (ART) terselia dan model Pembelajaran Pengukuhan (RL) untuk pengelasan corak. Secara khususnya, rangkaian ARTMAP Kabur (FAM) dan Pembelajaran-Q dijadikan sebagai tulang belakang dalam merekabentuk dan membina model-model hibrid. Satu model QFAM baharu terlebih dahulu diperkenalkan bagi menambahbaik prestasi pengelasan rangkaian FAM. Strategi pruning dimasukkan bagi mengurangkan kekompleksan QFAM. Bagi mengatasi isu ketidak-telusan, Algoritma Genetik (GA) digunakan bagi mengekstrak hukum kabur if-then daripada QFAM. Model yang terhasil iaitu QFAM-GA, dapat memberi ramalan berserta dengan huraian dengan hanya menggunakan bilangan antisiden yang sedikit. Bagi menambahkan lagi kebolehtahanan model-model Q-FAM, penggunaan sistem agenpelbagai telah dicadangkan. Hasilnya, model gugusan QFAM berasaskan agen dengan ukuran percaya dan kaedah rundingan baharu telah dicadangkan. Pelbagai jenis masalah tanda-aras telah digunakan bagi penilaian model-model gugusan dan individu berasaskan QFAM. Hasilnya telah dianalisa dan dibandingkan dengan FAM serta model-model yang dilaporkan dalam kajian terdahulu. Sebagai tambahan, dua daripada masalah dunia-nyata digunakan bagi menunjukkan kebolehan praktikal model hibrid. Keputusan akhir menunjukkan keberkesanan modul berasaskan QFAM dalam menerajui tugas-tugas pengelasan corak. ________________________________________________________________________________________________________________________ Pattern classification is one of the primary issues in various data mining tasks. In this study, the main research focus is on the design and development of hybrid models, combining the supervised Adaptive Resonance Theory (ART) neural network and Reinforcement Learning (RL) models for pattern classification. Specifically, the Fuzzy ARTMAP (FAM) network and Q-learning are adopted as the backbone for designing and developing the hybrid models. A new QFAM model is first introduced to improve the classification performance of FAM network. A pruning strategy is incorporated to reduce the complexity of QFAM. To overcome the opaqueness issue, a Genetic Algorithm (GA) is used to extract fuzzy if-then rules from QFAM. The resulting model, i.e. QFAM-GA, is able to provide predictions with explanations using only a few antecedents. To further improve the robustness of QFAM-based models, the notion of multi agent systems is employed. As a result, an agent-based QFAM ensemble model with a new trust measurement and negotiation method is proposed. A variety of benchmark problems are used for evaluation of individual and ensemble QFAM-based models. The results are analyzed and compared with those from FAM as well as other models reported in the literature. In addition, two real-world problems are used to demonstrate the practicality of the hybrid models. The outcomes indicate the effectiveness of QFAM-based models in tackling pattern classification tasks

Using Similarity Criteria to Make Negotiation Trade-Offs

This paper addresses the issues involved in software agents making trade-offs during automated negotiations in which they have information uncertainty and resource limitations. In particular, the importance of being able to make trade-offs in real-world applications is highlighted and a novel algorithm for performing trade-offs for multi-dimensional goods is developed. The algorithm uses the notion of fuzzy similarity in order to find negotiation solutions that are beneficial to both parties. Empirical results indicate the benefits and effectiveness of the trade-off algorithm in a range of negotiation situations

Optimal Control of Unknown Nonlinear System From Inputoutput Data

Optimal control designers usually require a plant model to design a controller. The problem is the controller\u27s performance heavily depends on the accuracy of the plant model. However, in many situations, it is very time-consuming to implement the system identification procedure and an accurate structure of a plant model is very difficult to obtain. On the other hand, neuro-fuzzy models with product inference engine, singleton fuzzifier, center average defuzzifier, and Gaussian membership functions can be easily trained by many well-established learning algorithms based on given input-output data pairs. Therefore, this kind of model is used in the current optimal controller design. Two approaches of designing optimal controllers of unknown nonlinear systems based on neuro-fuzzy models are presented in the thesis. The first approach first utilizes neuro-fuzzy models to approximate the unknown nonlinear systems, and then the feasible-direction algorithm is used to achieve the numerical solution of the Euler-Lagrange equations of the formulated optimal control problem. This algorithm uses the steepest descent to find the search direction and then apply a one-dimensional search routine to find the best step length. Finally several nonlinear optimal control problems are simulated and the results show that the performance of the proposed approach is quite similar to that of optimal control to the system represented by an explicit mathematical model. However, due to the limitation of the feasible-direction algorithm, this method cannot be applied to highly nonlinear and dimensional plants. Therefore, another approach that can overcome these drawbacks is proposed. This method utilizes Takagi-Sugeno (TS) fuzzy models to design the optimal controller. TS fuzzy models are first derived from the direct linearization of the neuro-fuzzy models, which is close to the local linearization of the nonlinear dynamic systems. The operating points are chosen so that the TS fuzzy model is a good approximation of the neuro-fuzzy model. Based on the TS fuzzy model, the optimal control is implemented for a nonlinear two-link flexible robot and a rigid asymmetric spacecraft, thus providing the possibility of implementing the well-established optimal control method on unknown nonlinear dynamic systems

A bi-level model of dynamic traffic signal control with continuum approximation

This paper proposes a bi-level model for traffic network signal control, which is formulated as a dynamic Stackelberg game and solved as a mathematical program with equilibrium constraints (MPEC). The lower-level problem is a dynamic user equilibrium (DUE) with embedded dynamic network loading (DNL) sub-problem based on the LWR model (Lighthill and Whitham, 1955; Richards, 1956). The upper-level decision variables are (time-varying) signal green splits with the objective of minimizing network-wide travel cost. Unlike most existing literature which mainly use an on-and-off (binary) representation of the signal controls, we employ a continuum signal model recently proposed and analyzed in Han et al. (2014), which aims at describing and predicting the aggregate behavior that exists at signalized intersections without relying on distinct signal phases. Advantages of this continuum signal model include fewer integer variables, less restrictive constraints on the time steps, and higher decision resolution. It simplifies the modeling representation of large-scale urban traffic networks with the benefit of improved computational efficiency in simulation or optimization. We present, for the LWR-based DNL model that explicitly captures vehicle spillback, an in-depth study on the implementation of the continuum signal model, as its approximation accuracy depends on a number of factors and may deteriorate greatly under certain conditions. The proposed MPEC is solved on two test networks with three metaheuristic methods. Parallel computing is employed to significantly accelerate the solution procedure