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Journal articles on the topic 'Adaptive intelligence model'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

Sternberg, Robert J. "A Theory of Adaptive Intelligence and Its Relation to General Intelligence." Journal of Intelligence 7, no. 4 (2019): 23. http://dx.doi.org/10.3390/jintelligence7040023.

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Intelligence typically is defined as consisting of “adaptation to the environment” or in related terms. Yet, it is not clear that “general intelligence” or g, traditionally conceptualized in terms of a general factor in a psychometrically-based hierarchical model of intelligence, provides an optimal way of defining intelligence as adaptation to the environment. Such a definition of adaptive intelligence would need to be biologically based in terms of evolutionary theory, would need to take into account the cultural context of adaptation, and would need to take into account whether thought and behavior labeled as “adaptively intelligent” actually contributed to the perpetuation of the human and other species, or whether it was indifferent or actually destructive to this perpetuation. In this article, I consider the similarities and differences between “general intelligence” and “adaptive intelligence,” as well as the implications especially of the differences.

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Mohammadalian, Sareh, Eslam Nazemi, and Mohammad Jafar Tarokh. "Propose a Conceptual Model of Adaptive Competitive Intelligence (ACI)." International Journal of Business Intelligence Research 4, no. 4 (2013): 22–32. http://dx.doi.org/10.4018/ijbir.2013100102.

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In recent years, competitive changes and pressure on business environment have increased importance of competitive fields. Competitive intelligence is one of the commercial tools available in this field. It extracts helpful information from competitive environment and, after accurate analysis, generates effective strategies for the organization. High speed of changes, uncertainty, complexity and so on are among the characteristics of competitive environments. Consequently, approach of competitive intelligence must be adaptable to any kind of changes occurring in the competitive environment. In this paper, a conceptual model was presented for adaptive competitive intelligence. The proposed model which was a conceptual model was evaluated along the paper and the results were discussed. Increasing sustainability of competitive power was one of the most important outcomes of the recommended model.

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Goel, Ashok K., and Eleni Stroulia. "Functional device models and model-Based diagnosis in adaptive design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10, no. 4 (1996): 355–70. http://dx.doi.org/10.1017/s0890060400001670.

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AbstractWe analyze the diagnosis task in the context of adaptive design and redesign of physical devices. We identify three types of diagnosis tasks that differ in the types of information they take as input: the design does not achieve a desired function of the device, the design results in an undesirable behavior, and a specific structural element in the design misbehaves. We describe a model-based approach for solving the diagnosis task in the context of adaptive design and redesign. This approach uses functional models that explicitly represent the device functions and use them to organize teleological and causal knowledge about the device. In particular, we describe a specific kind of functional model called structure—behavior—function (SBF) models in which the causal behaviors of the device are specified in terms of flow of substances through components. We illustrate the use of SBF models with three examples from Kritik2, a knowledge system that designs new devices by retrieving, diagnosing, and adapting old device designs.

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Li, Wei. "Neuron Network Model-Based Control System Model." Applied Mechanics and Materials 339 (July 2013): 143–46. http://dx.doi.org/10.4028/www.scientific.net/amm.339.143.

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When the control object complicate conventional PID control accuracy will be significantly reduced. In recent years, with the gradual improvement of the people of artificial intelligence theory, analog neural networks has been rapid development, the emergence of a large number of excellent algorithm and the means of achieving, from single neuron PID algorithm and with gain control neuron system PID algorithm, two aspects discusses the process of adaptive neuron PID algorithm to achieve accuracy improved adaptive neuron system controller PID algorithm based on this analysis.

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MERTOGUNO, J. S., and N. G. BOURBAKIS. "KYDON VISION SYSTEM: THE ADAPTIVE LEARNING MODEL." International Journal on Artificial Intelligence Tools 04, no. 04 (1995): 453–69. http://dx.doi.org/10.1142/s021821309500022x.

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In this paper, an adaptive learning model for an autonomous vision system multi-layers architecture, called Kydon, are presented, modeled, and analyzed. In particular two critical (deletion and saturation) points on the learning curve are evaluated. These points represent two extreme states on the learning process. The Kydon architecture consists of ‘k’ layers array processors. The lowest layers consists of lower-level processing layers, and the rest consists of higher-level processing layers. The interconnectivity of the PEs in each array is based on a full hexagonal mesh structure. Kydon uses graph models to represent and process the knowledge, extracted from the image. The knowledge base of Kydon is distributed among its PE’s. A unique model for evolving knowledge base has been developed especially for Kydon in order to provide it with some intelligence properties.

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Bendor, Jonathan, and Terry M. Moe. "An Adaptive Model of Bureaucratic Politics." American Political Science Review 79, no. 3 (1985): 755–74. http://dx.doi.org/10.2307/1956842.

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In this article we outline a new framework for the formal analysis of bureaucratic politics. It departs from standard neoclassical approaches, notably those of Niskanen (1971) and Peltzman (1976), in several important respects. First our approach explicitly models a system of three-way interaction among bureaus, politicians, and interest groups. Second, it allows for institutional features of each type of participant. Third, it is a model of dynamic process. Fourth, participants make choices adoptively rather than optimizing. Fifth, participants are only minimally informed.The result is a dynamic model of adaptive behavior, very much in the spirit of Simon's (1947) behavioral tradition, that offers a new perspective on political control, bureaucratic power, and the “intelligence of democracy.”

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Hanagud, S., B. J. Glass, and A. J. Calise. "Artificial intelligence-based model-adaptive approach to flexible structure control." Journal of Guidance, Control, and Dynamics 13, no. 3 (1990): 534–44. http://dx.doi.org/10.2514/3.25367.

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Kim, Joo-Chang, and Kyungyong Chung. "Neural-network based adaptive context prediction model for ambient intelligence." Journal of Ambient Intelligence and Humanized Computing 11, no. 4 (2018): 1451–58. http://dx.doi.org/10.1007/s12652-018-0972-3.

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Pradhan, Rabindra Kumar, Lalatendu Kesari Jena, and Sanjay Kumar Singh. "Examining the role of emotional intelligence between organizational learning and adaptive performance in Indian manufacturing industries." Journal of Workplace Learning 29, no. 3 (2017): 235–47. http://dx.doi.org/10.1108/jwl-05-2016-0046.

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Purpose The purpose of this study is to examine the relationship between organisational learning and adaptive performance. Furthermore, the study investigates the moderating role of emotional intelligence in the perspective of organisational learning for addressing adaptive performance of executives employed in manufacturing organisations. Design/methodology/approach The participants were selected through purposive sampling. The study has used established scales on organisational learning, emotional intelligence and adaptive performance to collect data from the respondents. Data were analysed through structural equation modelling using linear structural model (LISREL 8.72). Moderated regression analysis was carried out through a series of hierarchical models to test the hypotheses. The authors have followed the interaction graphs recommended by Aiken and West (1991) to check the moderating effect of emotional intelligence. Findings The result of the study indicates a significant relationship between organisational learning and adaptive performance. The significant moderation effect was observed in the interaction graph, wherein it was found that the relationship between organisational learning and adaptive performance was stronger among the executives with high levels of emotional intelligence and weaker for those having low levels of emotional intelligence. Originality/value The present study gains significance through highlighting the role of emotional intelligence in the perspective of organisational learning and, thus, offers insights to practitioners for addressing adaptive performance of employees.

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10

Hammell, Robert J., and Thomas Sudkamp. "An adaptive hierarchical fuzzy model." Expert Systems with Applications 11, no. 2 (1996): 125–36. http://dx.doi.org/10.1016/0957-4174(96)00040-1.

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Dhanalakshmi, R., and T. Sri Devi. "Adaptive cognitive intelligence in analyzing employee feedback using LSTM." Journal of Intelligent & Fuzzy Systems 39, no. 6 (2020): 8069–78. http://dx.doi.org/10.3233/jifs-189129.

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Cognitive computing is the mirroring of human brain and this is made possible by using natural language processing, pattern recognition and data mining. By mirroring the human brain (Cognitive computing system), helps to solve some of the complicated problems without much of human supervision. In the fast-changing world, the major challenge every organization facing is difficulty in retaining its employees. Employees may leave an organization due to low salary, overwork, lack of opportunities and recognition, work culture, work-life imbalance etc. Better ways to retain employees is to understand their requirements and fulfill them. The proposed employee feedback sentiment analysis system collects the employee feedback reviews from open forums and perform sentiment analysis using Recurrent Neural Network – Long Short-term Memory (RNN-LSTM) algorithm. On performing Sentiment analysis, employee review comments are classified as Positive or Negative. A report is generated and sent to the HR of the organization as webapp or mobile app. The report has total number of positive and negative comments and positive and negative counts with respect to salary, work pressure etc. With the report, the organization can arrive at identifying social sentiments of their brand and may take corrective actions to retain employees which benefits both organization and employees. This paper also captures the performance of various models in training and predicting the employee feedback dataset and the models evaluated are Logistic Regression, Support Vector Machine, Random Forest Classifier, AdaBoost Classifier, Gradient Boosting Classifier, Decision Tree Classifier and Gaussian Naïve Bayes. The classification report and accuracy of each model is captured. The dataset size was gradually increased from 200 to 1000 and accuracy was predicted for each model. It was identified that the accuracy of machine learning algorithms was ranging between 66% to 85%. On training RNN-LSTM algorithm with dataset of size 30 k, the accuracy was 88%. It was identified that Deep learning algorithm RNN-LSTM performs better with huge dataset. Increasing dataset size still increase the performance of RNN-LSTM algorithm in training and prediction. Thus, the objective function of the proposed model to perform sentiment analysis on employee feedback review comments is achieved successfully.

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Kam, Moshe, Ari Naim, and Kevin Atteson. "The symmetric adaptive resonance theoretic model (SMART)." Neural Networks 1 (January 1988): 103. http://dx.doi.org/10.1016/0893-6080(88)90142-6.

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Kothamasu, Ranganath, and Samuel H. Huang. "Adaptive Mamdani fuzzy model for condition-based maintenance." Fuzzy Sets and Systems 158, no. 24 (2007): 2715–33. http://dx.doi.org/10.1016/j.fss.2007.07.004.

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Blishun, A. F. "Fuzzy adaptive learning model of decision-making process." Fuzzy Sets and Systems 18, no. 3 (1986): 273–82. http://dx.doi.org/10.1016/0165-0114(86)90006-0.

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15

Kollman, Ken, John H. Miller, and Scott E. Page. "Adaptive Parties in Spatial Elections." American Political Science Review 86, no. 4 (1992): 929–37. http://dx.doi.org/10.2307/1964345.

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We develop a model of two-party spatial elections that departs from the standard model in three respects: parties' information about voters' preferences is limited to polls; parties can be either office-seeking or ideological; and parties are not perfect optimizers, that is, they are modelled as boundedly rational adaptive actors. We employ computer search algorithms to model the adaptive behavior of parties and show that three distinct search algorithms lead to similar results. Our findings suggest that convergence in spatial voting models is robust to variations in the intelligence of parties. We also find that an adaptive party in a complex issue space may not be able to defeat a well-positioned incumbent.

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CHIANG, JUNG-HSIEN. "A FUZZY ROUTE GUIDANCE MODEL FOR INTELLIGENT IN-VEHICLE NAVIGATION SYSTEMS." International Journal on Artificial Intelligence Tools 08, no. 02 (1999): 229–37. http://dx.doi.org/10.1142/s0218213099000154.

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This paper presents an adaptive fuzzy clustering model that can be used to identify nature subgroups of links as well as priority memberships in a route guidance system. The fuzzy route guidance model, inspired by the fuzzy clustering technique, provides an adaptive and efficient alternative to traditional fixed costs route guidance methods. Three specific objectives underlie the presentation of the fuzzy route guidance model in this paper. The first is to describe a general overview of the in-vehicle navigation system, and the second is to introduce the fuzzy route guidance model based on adaptive fuzzy clustering and least cost problem. The third part is to demonstrate that the proposed model is able to perform route guidance in road test.

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Salehi, Hadi, Javad Vahidi, and Homayun Motameni. "A Robust Hybrid Filter Based on Evolutionary Intelligence and Fuzzy Evaluation." International Journal of Image and Graphics 18, no. 04 (2018): 1850023. http://dx.doi.org/10.1142/s0219467818500237.

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In this paper, a novel denoising method based on wavelet, extended adaptive Wiener filter and the bilateral filter is proposed for digital images. Production of mode is accomplished by the genetic algorithm. The proposed extended adaptive Wiener filter has been developed from the adaptive Wiener filter. First, the genetic algorithm suggest some hybrid models. The attributes of images, including peak signal to noise ratio, signal to noise ratio and image quality assessment are studied. Then, in order to evaluate the model, the values of attributes are sent to the Fuzzy deduction system. Simulations and evaluations mentioned in this paper are accomplished on some standard images such as Lena, boy, fruit, mandrill, Barbara, butterfly, and boat. Next, weaker models are omitted by studying of the various models. Establishment of new generations performs in a form that a generation emendation is carried out, and final model has a more optimum quality compared to each two filters in order to obviate the noise. At the end, the results of this system are studied so that a comprehensive model with the best performance is to be found. Experiments show that the proposed method has better performance than wavelet, bilateral, Butterworth, and some other filters.

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Jiang, Rui, and Kueiming Lo. "OPTIMAL ADAPTIVE CONTROLLER FOR MULTIDIMENSIONAL ARMAX MODEL." Cybernetics and Systems 38, no. 2 (2007): 141–54. http://dx.doi.org/10.1080/01969720601139009.

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Choi, Younyoung, and Cayce McClenen. "Development of Adaptive Formative Assessment System Using Computerized Adaptive Testing and Dynamic Bayesian Networks." Applied Sciences 10, no. 22 (2020): 8196. http://dx.doi.org/10.3390/app10228196.

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Online formative assessments in e-learning systems are increasingly of interest in the field of education. While substantial research into the model and item design aspects of formative assessment has been conducted, few software systems embodied with a psychometric model have been proposed to allow us to adaptively implement formative assessments. This study aimed to develop an adaptive formative assessment system, called computerized formative adaptive testing (CAFT) by using artificial intelligence methods based on computerized adaptive testing (CAT) and Bayesian networks as learning analytics. CAFT can adaptively administer personalized formative assessment to a learner by dynamically selecting appropriate items and tests aligned with the learner’s ability. Forty items in an item bank were evaluated by 410 learners, moreover, 1000 learners were recruited for a simulation study and 120 learners were enrolled to evaluate the efficiency, validity, and reliability of CAFT in an application study. The results showed that, through CAFT, learners can adaptively take item s and tests in order to receive personalized diagnostic feedback about their learning progression. Consequently, this study highlights that a learning management system which integrates CAT as an artificially intelligent component is an efficient educational evaluation tool for a remote personalized learning service.

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Hou, Dongdong, Yang Cong, Gan Sun, Ji Liu, and Xiaowei Xu. "Anomaly detection via adaptive greedy model." Neurocomputing 330 (February 2019): 369–79. http://dx.doi.org/10.1016/j.neucom.2018.09.080.

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KINOUCHI, YASUO. "A LOGICAL MODEL OF CONSCIOUSNESS ON AN AUTONOMOUSLY ADAPTIVE SYSTEM." International Journal of Machine Consciousness 01, no. 02 (2009): 235–42. http://dx.doi.org/10.1142/s1793843009000219.

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22

Hasan, Sajib. "Adaptive Fitts for Adaptive Interface." AIUB Journal of Science and Engineering (AJSE) 17, no. 2 (2018): 51–58. http://dx.doi.org/10.53799/ajse.v17i2.9.

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Adaptive interface would enable Human Computer Interaction apply machine learning to cope with human carelessness (mistakes), understand user performance level and provide an interaction interface accordingly. This study tends to translate the theoretical issues of human task into working model by investigating and implementing the predicting equation of human psychomotor behavior to a rapid and aimed movement, developed by Paul Fitt in 1954. The study finds logarithmic speed-accuracy trade-off and predict user performance in a common task “point-select” using common input device mouse. The performance of user is visualized as an evidence and this visualization make a valuable step toward understanding the change required in user interface to make the interface adaptive and consistent. It proposed a method of calculating the amount of change required through learning; add extension to the theory of machine intelligence and increase knowledge of Fitts applicability in terms of machine learning.

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Kim, Yong Soo, and Sunanda Mitra. "An adaptive integrated fuzzy clustering model for pattern recognition." Fuzzy Sets and Systems 65, no. 2-3 (1994): 297–310. http://dx.doi.org/10.1016/0165-0114(94)90026-4.

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HU, JIANJUEN J., JERRY E. PRATT, CHEE-MENG CHEW, HUGH M. HERR, and GILL A. PRATT. "VIRTUAL MODEL BASED ADAPTIVE DYNAMIC CONTROL OF A BIPED WALKING ROBOT." International Journal on Artificial Intelligence Tools 08, no. 03 (1999): 337–48. http://dx.doi.org/10.1142/s0218213099000221.

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The robustness of bipedal walking robots can be enhanced by the use of adaptive control techniques. In this paper, we extend a previous control approach. "Virtual Model Control" (VMC) [6] to create "Adaptive Virtual Model Control" (AVMC). The adaptation compensates for external disturbances and unmodelled dynamics, enhancing robustness in the control of height, pitch, and forward speed. The state machine used to modulate the virtual model components and to select the appropriate virtual to physical transformations (as in traditional VMC) is also used to inform the adaptation about the robot's changing configuration. The design procedure for AVMC is described in this paper and simulation results are presented for a planar walking biped.

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Nguyen, Tu Trung, and Kien Dinh. "An artificial intelligence approach for concrete hardened property estimation." Journal of Science and Technology in Civil Engineering (STCE) - NUCE 14, no. 2 (2020): 40–52. http://dx.doi.org/10.31814/stce.nuce2020-14(2)-04.

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An alternative method using Artificial Intelligence (AI) to predict the 28-day strength of concrete from its primary ingredients is presented in this research. A series of 424 data samples collected from a previous study were employed for developing, testing, and validation of Adaptive Neuro-Fuzzy Inference System (ANFIS) models. Seven mix parameters, namely Cement, Blast Furnace Slag, Fly Ash, Water, Superplasticizer, Coarse Aggregate, and Fine Aggregate were used as the inputs of the models while the output was the 28-day compressive strength of concrete. In the first step, different models with various input membership functions were explored and compared to obtain an optimal ANFIS model. In the second step, that model was utilized to predict the compressive strength value for each concrete sample, and to compare with those obtained from the compressive test in laboratory. The results showed that the selected ANFIS model can be used as a reliable tool for predicting the compressive strength of concrete with Root Mean Squared Error values of 5.97 MPa and 7.73 MPa, respectively, for the training and test sets. In addition, the sensitivity analysis results revealed that the accuracy of the proposed model improved with an increase in the number of input parameters/variables. Keywords: artificial intelligence; adaptive neuro-fuzzy inference system; concrete strength; sensitivity analysis.

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Duchanoy, Carlos A., Hiram Calvo, and Marco A. Moreno-Armendáriz. "ASAMS: An Adaptive Sequential Sampling and Automatic Model Selection for Artificial Intelligence Surrogate Modeling." Sensors 20, no. 18 (2020): 5332. http://dx.doi.org/10.3390/s20185332.

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Surrogate Modeling (SM) is often used to reduce the computational burden of time-consuming system simulations. However, continuous advances in Artificial Intelligence (AI) and the spread of embedded sensors have led to the creation of Digital Twins (DT), Design Mining (DM), and Soft Sensors (SS). These methodologies represent a new challenge for the generation of surrogate models since they require the implementation of elaborated artificial intelligence algorithms and minimize the number of physical experiments measured. To reduce the assessment of a physical system, several existing adaptive sequential sampling methodologies have been developed; however, they are limited in most part to the Kriging models and Kriging-model-based Monte Carlo Simulation. In this paper, we integrate a distinct adaptive sampling methodology to an automated machine learning methodology (AutoML) to help in the process of model selection while minimizing the system evaluation and maximizing the system performance for surrogate models based on artificial intelligence algorithms. In each iteration, this framework uses a grid search algorithm to determine the best candidate models and perform a leave-one-out cross-validation to calculate the performance of each sampled point. A Voronoi diagram is applied to partition the sampling region into some local cells, and the Voronoi vertexes are considered as new candidate points. The performance of the sample points is used to estimate the accuracy of the model for a set of candidate points to select those that will improve more the model’s accuracy. Then, the number of candidate models is reduced. Finally, the performance of the framework is tested using two examples to demonstrate the applicability of the proposed method.

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Wang, Hongli, Bin Guo, Jiaqi Liu, Sicong Liu, Yungang Wu, and Zhiwen Yu. "Context-aware Adaptive Surgery." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 3 (2021): 1–22. http://dx.doi.org/10.1145/3478073.

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Deep Neural Networks (DNNs) have made massive progress in many fields and deploying DNNs on end devices has become an emerging trend to make intelligence closer to users. However, it is challenging to deploy large-scale and computation-intensive DNNs on resource-constrained end devices due to their small size and lightweight. To this end, model partition, which aims to partition DNNs into multiple parts to realize the collaborative computing of multiple devices, has received extensive research attention. To find the optimal partition, most existing approaches need to run from scratch under given resource constraints. However, they ignore that resources of devices (e.g., storage, battery power), and performance requirements (e.g., inference latency), are often continuously changing, making the optimal partition solution change constantly during processing. Therefore, it is very important to reduce the tuning latency of model partition to realize the real-time adaption under the changing processing context. To address these problems, we propose the Context-aware Adaptive Surgery (CAS) framework to actively perceive the changing processing context, and adaptively find the appropriate partition solution in real-time. Specifically, we construct the partition state graph to comprehensively model different partition solutions of DNNs by import context resources. Then "the neighbor effect" is proposed, which provides the heuristic rule for the search process. When the processing context changes, CAS adopts the runtime search algorithm, Graph-based Adaptive DNN Surgery (GADS), to quickly find the appropriate partition that satisfies resource constraints under the guidance of the neighbor effect. The experimental results show that CAS realizes adaptively rapid tuning of the model partition solutions in 10ms scale even for large DNNs (2.25x to 221.7x search time improvement than the state-of-the-art researches), and the total inference latency still keeps the same level with baselines.

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Shah, Rohan, and Timothy Sands. "Comparing Methods of DC Motor Control for UUVs." Applied Sciences 11, no. 11 (2021): 4972. http://dx.doi.org/10.3390/app11114972.

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Adaptive and learning methods are proposed and compared to control DC motors actuating control surfaces of unmanned underwater vehicles. One type of adaption method referred to as model-following is based on algebraic design, and it is analyzed in conjunction with parameter estimation methods such as recursive least squares, extended least squares, and batch least squares. Another approach referred to as deterministic artificial intelligence uses the process dynamics defined by physics to control output to track a necessarily specified autonomous trajectory (sinusoidal versions implemented here). In addition, one instantiation of deterministic artificial intelligence uses 2-norm optimal feedback learning of parameters to modify the control signal, while another instantiation is presented with proportional plus derivative adaption. Model-following and deterministic artificial intelligence are simulated, and respective performance metrics for transient response and input tracking are evaluated and compared. Deterministic artificial intelligence outperformed the model-following approach in minimal peak transient value by a percent range of approximately 2–70%, but model-following achieved at least 29% less error in input tracking than deterministic artificial intelligence. This result is surprising and not in accordance with the recently published literature, and the explanation of the difference is theorized to be efficacy with discretized implementations.

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Piltan, Farzin, Ali Badri, Javad Meigolinedjad, and Mohammad Keshavarz. "Adaptive Artificial Intelligence Based Model Base Controller: Applied to Surgical Endoscopy Telemanipulator." International Journal of Intelligent Systems and Applications 5, no. 9 (2013): 103–15. http://dx.doi.org/10.5815/ijisa.2013.09.12.

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Wang, Nan, Dongxuan Wang, and Yuting Zhang. "Design of an adaptive examination system based on artificial intelligence recognition model." Mechanical Systems and Signal Processing 142 (August 2020): 106656. http://dx.doi.org/10.1016/j.ymssp.2020.106656.

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Wang, Zheng. "A swarm intelligence learning model of adaptive incentive protocols for P2P networks." International Journal of Communication Networks and Distributed Systems 20, no. 2 (2018): 168. http://dx.doi.org/10.1504/ijcnds.2018.089770.

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Wang, Zheng. "A swarm intelligence learning model of adaptive incentive protocols for P2P networks." International Journal of Communication Networks and Distributed Systems 20, no. 2 (2018): 168. http://dx.doi.org/10.1504/ijcnds.2018.10010389.

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Horváth, László, and Imre J. Rudas. "Possibilities for Application of Associative Objects with Built-in Intelligence in Engineering Modeling." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 5 (2004): 544–52. http://dx.doi.org/10.20965/jaciii.2004.p0544.

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An analysis is given in this paper for definition of features of environment adaptive model objects having the capability of description of mutual effects of engineering objects inside and associative engineering objects from the outside world. The final aim of the research is development of environment adaptive model objects for integration in or communication with product models. The model objects, targeted by the reported research, are able to carry and interpret all information and knowledge necessary for their processing. Creation and application of objects during engineering decision procedures as well as revision of earlier decisions during development of products are considered. This paper discusses concepts and features of intelligent model objects as they applied for environment-active modeling. It also reports an analysis of effects of changes in the model environment on model of intelligent engineering object. Following this, paper explains relationships within model objects for the purpose of engineering activities in mechanical systems. Finally, some considerations about implementation of the proposed modeling are concluded.

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Xu, Xiaowei, and Ting Bu. "An Adaptive Parameter Choosing Approach for Regularization Model." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 08 (2018): 1859013. http://dx.doi.org/10.1142/s0218001418590139.

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The choice of regularization parameters is a troublesome issue for most regularization methods, e.g. Tikhonov regularization method, total variation (TV) method, etc. An appropriate parameter for a certain regularization approach can obtain fascinating results. However, general methods of choosing parameters, e.g. Generalized Cross Validation (GCV), cannot get more precise results in practical applications. In this paper, we consider exploiting the more appropriate regularization parameter within a possible range, and apply the estimated parameter to Tikhonov model. In the meanwhile, we obtain the optimal regularization parameter by the designed criterions and evaluate the recovered solution. Moreover, referred parameter intervals and designed criterions of this method are also presented in the paper. Numerical experiments demonstrate that our method outperforms GCV method evidently for image deblurring application. Especially, the parameter estimation algorithm can also be applied to many regularization models related to pattern recognition, artificial intelligence, computer vision, etc.

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Chang, Shi-Kuo, Han Zhong Zheng, Tian Yi Cui, Nannan Wen, Hao Zhai, and Lu Zhang. "A Computation Model for Senior Citizen Health Self-Care." International Journal of Software Engineering and Knowledge Engineering 30, no. 04 (2020): 483–501. http://dx.doi.org/10.1142/s021819402040001x.

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A computation model is proposed for senior citizen health self-care through adaptive multi-level computation cycles according to the principles of slow intelligence. An experimental system, called the TDR system, is being implemented on a smart phone to serve as the test bed for the proposed approach. In this paper, we describe the computation model, the basic concepts and the prototype experimental system. The main theoretical concept is centered on adaptive multi-level computation cycles, events and event icons. Further experimental as well as theoretical investigations of the proposed computation model are discussed.

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Li, Guiji, Manman Peng, Ke Nai, Zhiyong Li, and Keqin Li. "Multi-view correlation tracking with adaptive memory-improved update model." Neural Computing and Applications 32, no. 13 (2019): 9047–63. http://dx.doi.org/10.1007/s00521-019-04413-4.

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Lin, Yong-Huang, Pin-Chan Lee, and Ta-Peng Chang. "Adaptive and high-precision grey forecasting model." Expert Systems with Applications 36, no. 6 (2009): 9658–62. http://dx.doi.org/10.1016/j.eswa.2008.12.009.

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Skorina, Erik H., Ming Luo, Weijia Tao, Fuchen Chen, Jie Fu, and Cagdas D. Onal. "Adapting to Flexibility: Model Reference Adaptive Control of Soft Bending Actuators." IEEE Robotics and Automation Letters 2, no. 2 (2017): 964–70. http://dx.doi.org/10.1109/lra.2017.2655572.

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Kozel, Tomas, and Milos Stary. "Adaptive stochastic management of the storage function for a large open reservoir using an artificial intelligence method." Journal of Hydrology and Hydromechanics 67, no. 4 (2019): 314–21. http://dx.doi.org/10.2478/johh-2019-0021.

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Abstract The design and evaluation of algorithms for adaptive stochastic control of reservoir function of the water reservoir using artificial intelligence methods (learning fuzzy model and neural networks) are described in this article. This procedure was tested on an artificial reservoir. Reservoir parameters have been designed to cause critical disturbances during the control process, and therefore the influences of control algorithms can be demonstrated in the course of controlled outflow of water from the reservoir. The results of the stochastic adaptive models were compared. Further, stochastic model results were compared with a resultant course of management obtained using the method of classical optimisation (differential evolution), which used stochastic forecast data from real series (100% forecast). Finally, the results of the dispatcher graph and adaptive stochastic control were compared. Achieved results of adaptive stochastic management provide inspiration for continuing research in the field.

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Wisker, Zazli Lily, and Athanasios Poulis. "Emotional Intelligence – Sales Performance Relationship: A Mediating Role of Adaptive Selling Behaviour." International Journal of Management and Economics 43, no. 1 (2014): 32–52. http://dx.doi.org/10.1515/ijme-2015-0002.

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Abstract In this study, we examined the impact of emotional intelligence on sales performance. We posited that the impact of emotional intelligence (EI) on sales performance was mediated by adaptive selling behaviour (ASB). Data were collected from 281 sales people in the financial industries in Malaysia via the WLEIS emotional intelligence scale and ADAPTS adaptive selling behaviour scale, and were quantitatively analysed using structural equation modelling (SEM). Results were in keeping with the model. Three domains of EI were not found to impact sales performance directly but through ASB. Theoretical implications and managerial ramifications were also discussed.

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LI, QI, JIEPING YE, MIN LI, and CHANDRA KAMBHAMETTU. "ADAPTIVE APPEARANCE BASED FACE RECOGNITION." International Journal on Artificial Intelligence Tools 17, no. 01 (2008): 175–93. http://dx.doi.org/10.1142/s0218213008003832.

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In this paper, we present an adaptive appearance based face recognition framework that combines the efficiency of global approaches and the robustness of local approaches together. The framework uses a novel eye locator to select an appropriate scheme for appearance based recognition. The eye locator first locates eye candidates via a new strength assignment, determined by the dissimilarity between the local appearance of an image point and the appearance of its neighboring points. Then the eye locator applies a simple but flexible model (half-circle snake) to the local context of the eye candidates in order to either refine the location of an eye candidate or discard non-eye candidates. We show the performance of our framework by testing on challenging face datasets containing extreme expressions, severe occlusions, and varied lighting conditions.

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Udoyen, Nsikan, and David W. Rosen. "Reusability-based selection of parametric finite element analysis models." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 23, no. 2 (2009): 197–214. http://dx.doi.org/10.1017/s0890060409000134.

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AbstractA selection method to support adaptive reuse of parametric finite element analysis (FEA) models is introduced in this paper. Adaptive reuse of engineering artifacts such as FEA models is common in product design, but difficult to automate because of the need to integrate new information. The proposed method factors reusability into selection by evaluating models based on comparative estimates of effort involved in adapting them for reuse to model a query problem. The method is developed for FEA models of component-based designs. FEA modeling of electronic chip packages is used to illustrate the method's usefulness. We conclude with a discussion on the method's advantages and limitations and highlight important issues for further research.

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Papazoglou, Aimilia, Lisa A. Jacobson, and T. Andrew Zabel. "More than Intelligence: Distinct Cognitive/Behavioral Clusters Linked to Adaptive Dysfunction in Children." Journal of the International Neuropsychological Society 19, no. 2 (2013): 189–97. http://dx.doi.org/10.1017/s1355617712001191.

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AbstractImpairments in adaptive functioning are frequently associated with intellectual disability (ID); however, adaptive dysfunction can be seen in many individuals with a variety of neurological conditions without ID. The extent to which other variables may be associated with adaptive dysfunction is unclear. In a mixed clinical sample of children (n = 348) consecutively referred for neuropsychological evaluation, the majority were rated as showing weak adaptive skills (ABAS-II, >1 SD below the mean; 71%), with a substantial proportion evidencing frank impairment (>2 SD below the mean, 45%). We examined patterns of scores on measures of intelligence (WISC-IV) and behavioral/affective dysregulation (BRIEF and BASC-2). Using hierarchical cluster analysis, a four cluster model yielded the most appropriate fit and adaptive functioning was subsequently examined across clusters. As expected, adaptive functioning was most intact in the cluster characterized by average IQ and minimal behavioral dysregulation. Other clusters were marked by adaptive dysfunction and distinguished by sub-average intellectual functioning and varying behavioral/emotional dysregulation. In contrast to traditional views associating low IQ with adaptive dysfunction, adaptive impairment was comparable between the cluster characterized by low intelligence and the cluster with average intelligence but significant behavioral dysregulation. These data suggest that adaptive functioning should be considered across various cognitive/behavioral conditions. (JINS, 2013, 19, 1–9)

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Halpern, Diane F., and Dana S. Dunn. "Critical Thinking: A Model of Intelligence for Solving Real-World Problems." Journal of Intelligence 9, no. 2 (2021): 22. http://dx.doi.org/10.3390/jintelligence9020022.

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Most theories of intelligence do not directly address the question of whether people with high intelligence can successfully solve real world problems. A high IQ is correlated with many important outcomes (e.g., academic prominence, reduced crime), but it does not protect against cognitive biases, partisan thinking, reactance, or confirmation bias, among others. There are several newer theories that directly address the question about solving real-world problems. Prominent among them is Sternberg’s adaptive intelligence with “adaptation to the environment” as the central premise, a construct that does not exist on standardized IQ tests. Similarly, some scholars argue that standardized tests of intelligence are not measures of rational thought—the sort of skill/ability that would be needed to address complex real-world problems. Other investigators advocate for critical thinking as a model of intelligence specifically designed for addressing real-world problems. Yes, intelligence (i.e., critical thinking) can be enhanced and used for solving a real-world problem such as COVID-19, which we use as an example of contemporary problems that need a new approach.

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El Afia, Abdellatif, Oussama Aoun, and Salvador Garcia. "Adaptive cooperation of multi-swarm particle swarm optimizer-based hidden Markov model." Progress in Artificial Intelligence 8, no. 4 (2019): 441–52. http://dx.doi.org/10.1007/s13748-019-00183-1.

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Jami’in, Mohammad Abu, Khairul Anam, Riries Rulaningtyas, Urip Mudjiono, Adianto Adianto, and Hui-Ming Wee. "Hierarchical linear and nonlinear adaptive learning model for system identification and prediction." Applied Intelligence 50, no. 6 (2020): 1699–710. http://dx.doi.org/10.1007/s10489-019-01615-0.

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Lam, Ka-Man, and Ho-Fung Leung. "A Trust/Honesty Model with Adaptive Strategy for Multiagent Semi-Competitive Environments." Autonomous Agents and Multi-Agent Systems 12, no. 3 (2005): 293–359. http://dx.doi.org/10.1007/s10458-005-4984-y.

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Schröder-Schetelig, Johannes, Poramate Manoonpong, and Florentin Wörgötter. "Using efference copy and a forward internal model for adaptive biped walking." Autonomous Robots 29, no. 3-4 (2010): 357–66. http://dx.doi.org/10.1007/s10514-010-9199-7.

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Carmona, P., J. L. Castro, and J. M. Zurita. "Contradiction sensitive fuzzy model-based adaptive control." International Journal of Approximate Reasoning 30, no. 2 (2002): 107–29. http://dx.doi.org/10.1016/s0888-613x(02)00065-8.

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James, D. J. G., F. Boehringer, K. J. Burnham, and D. G. Copp. "Adaptive driver model using a neural network." Artificial Life and Robotics 7, no. 4 (2004): 170–76. http://dx.doi.org/10.1007/bf02471201.

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